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AI Salary Benchmarks for Poland in 2025

Based on analyzing 250+ AI Job Openings, and 600+ total job openings across Data, Backend, and Frontend
AI/ML Job Market Dashboard - Poland

AI/ML Job Market Dashboard - Poland

255
Total Jobs
24,495 PLN
Average Salary
24,000 PLN
Median Salary
8K - 63K PLN
Salary Range

Salary by Seniority Level

Average Salary by Location

Top Skills in Demand

Most Common Job Titles

Table of Contents
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Meet The Authors

Maciej Gos
Chief Architect
ITMAGINATION LinkedIn
Hisham Itani
Head of Marketing
ITMAGINATION LinkedIn
Have any questions? Feel free to reach out to us!

Executive summary

Based on a comprehensive analysis of 250+ AI/ML job postings and 600+ overall job postings analyzed in May 2025, this report provides detailed insights into salary trends, geographic distribution, skill demands, and market dynamics in Poland's artificial intelligence and machine learning sector.

The analysis reveals a vibrant and competitive market with significant opportunities for companies looking to expand their AI/ML operations in Poland. 

Below are brief observations and recommendations which we will dive into in the full report.

Compensation and Sourcing:

  • Salary Ranges: Expect salary ranges to be flexible, with an average spread of 6,239 PLN per month. Be prepared for negotiations.
  • Remote Work: Remote positions offer the highest average salaries (26,159 PLN monthly). Consider offering remote or at least flexible hybrid options to attract top candidates.
  • Geographic Hotspots: Warsaw is the leader for the total number of AI/ML jobs, but other cities like Kraków and Wrocław offer slightly higher average salaries.
  • Contract vs. Employee: B2B (contractor) roles command significantly higher "take-home" compensation than UoP (employment contract) roles. Be mindful of this when structuring your offers.

Skills and Technology:

  • Python is an Essential Skill: Python is non-negotiable, appearing in >66% of all job postings. It’s the baseline requirement for most AI/ML roles.
  • Cloud Expertise: Azure and AWS skills are in high demand. Beyond knowing how to code, it’s important to find candidates who know how to use cloud platforms for your benefit. The cost savings skilled developers can bring by optimizing cloud deployments can largely outweigh any salary premium you would need to pay.
  • Large Skill Diversity: AI/ML roles require a wider variety of skills compared to traditional tech roles and these are constantly evolving. Be prepared to assess candidates on multiple dimensions and be flexible with the terminology in your job posts. Reassess your requirements often.

Hiring Considerations:

  • Talent Competition: The high volume of mid-level positions means competition is stiff. Be prepared to move quickly on qualified candidates in this bracket.
  • Negotiation: Expect candidates to negotiate and put flexible salary bands in place.
  • Entry-Level Talent: A significant portion of the talent market is entry-level but only a handful of companies are hiring for junior roles (see Methodology below). Consider developing internal training programs to grow this talent pool, including re-skilling and upskilling.

AI/ML Positions vs. Others (Data, Backend, Frontend):

  • Skillset Variance: AI/ML roles have a significantly higher variety and number of skills associated with the roles. This is due to the continuing maturity of the market, while other roles are already mature and have consolidated.
  • Differences in Salaries:  AI/ML roles offer a premium of 1,548 PLN (~8% in relative terms) compared to other tech areas. This is not equal among areas - it amounts to a ~12% premium over frontend positions but only a ~3% premium over data positions.
  • Wider Salary Ranges: AI/ML positions show not only higher average salaries but also wider salary ranges, suggesting greater variability in compensation packages and a market that is still exploring at what level the salaries should be for such roles.

By understanding these market dynamics and tailoring your hiring strategies accordingly, you can successfully build your AI/ML team in Poland.


Notes From the Authors

Methodology

  • Areas: We analyzed 255 AI/ML job posts, and 617 total job posts. The other areas analyzed were Data, Backend, and Frontend.
  • Sources: The data was obtained from multiple job portals: NoFluffJobs, JustJoinIT, BulldogJob, and from job postings on LinkedIn.
  • Junior Roles: Even without actively filtering our junior job opportunities, there were only ~10 job postings in the AI/ML category marked for Junior Experience Levels and ~15 job postings across all other categories, so we excluded them in the analysis across all areas, including Data, Backend, and Frontend.
  • Employment Contracts: we assumed a 30% additional company cost to normalize the salary ranges for Employment Contracts (Umowa o Pracę or UoP). Only <10% of all job openings were under UoP.
  • Hourly Rates: For jobs with hourly rates, we assumed 160 working hours per month to calculate the equivalent monthly salary.
  • Manual Adjustments: There is a big mix and cross-pollination between Data and AI/ML in terms of categorization, we edited ~5% of all the job postings categorization manually. E.g. Data Science positions which were under Data 🡪 AI/ML. Data Engineering positions with just some AI who were categorized under AI/ML 🡪 Data.

Overview

Poland has emerged as a key player in the European AI ecosystem. A strong talent pool, competitive cost structure, and growing investment in AI research and innovation have made it a strategic location for both local startups and global enterprises. As a result, the demand for AI professionals is on the rise, not only in Warsaw but increasingly in other cities and through remote-first roles.

Salaries in AI-related fields across Poland vary significantly based on a combination of factors such as experience level, role specialization, geographic location, company size, and specific technical skills required.

This report breaks down compensation by experience, specialization, location, and employer profile to offer a clear, data-informed view of the current market.

Salary Ranges by Seniority

As in most tech fields, seniority is one of the strongest indicators of how much professionals in AI can expect to earn. However, in Poland, salary levels are also shaped by the type of contract (UoP or B2B), the city where the role is based, and the seniority of the position within the organization.

Here’s a breakdown of the average monthly salaries for different experience levels in AI roles:

Mid/Regular Level (2–7 years of experience)

Mid-level specialists typically see a significant step up in both responsibility and pay. Salaries in this range fall between 14,000 and 19,700 PLN/month on a full-time employment contract (UoP), and up to 21,000 PLN/month for those working on B2B contracts.

At this level, professionals are often expected to take ownership of components such as ML pipelines, model optimization, or integrations with production systems. Specialists in high-demand areas like NLP or MLOps often land at the higher end of the range.

Senior Level (5+ Years of Experience)

Senior AI professionals are among the most in-demand, and that’s reflected in their compensation. Salaries typically range between 18,000 and 23,600 PLN/month on UoP, with B2B contracts going up to 26,900 PLN/month.

Senior team members often take on system architecture, team mentoring, and cross-functional collaboration. In Warsaw and among leading tech firms, salaries toward the top of this range are increasingly common.

Lead, Principal, and Manager Roles (8+ Years of Experience)

For professionals stepping into strategic or leadership roles, monthly salaries often exceed 25,000 PLN, with the average monthly salary reaching nearly 28,400 PLN for managers.

These positions come with responsibilities like setting AI direction, managing teams, and aligning machine learning initiatives with broader business goals. 

Interestingly, the salary ranges for Managers are often lower than those of Leads/Architects, and even below those of senior engineers in some cases. In larger companies or international organizations, additional bonuses, stock options, or international exposure can increase the total compensation package.


Key Skills Influencing Salaries

In Poland’s AI job market, what you know matters just as much as how long you’ve been working. While experience and seniority shape salary ranges, the specific skills a professional brings to the table, technical, domain-specific, or interpersonal, can have a major impact on compensation.

Python Dominates Technical Requirements

Python emerges as the overwhelming leader with 169 mentions across job postings, appearing in >2/3 of all positions.

This dominance reflects Python's role as the de facto standard for AI/ML development, data science, and machine learning applications. Jobs requiring Python skills average 24,773 PLN monthly, representing a 250 PLN premium over the market average.

While Python remains a universal baseline, the surrounding tech stacks reflect each company type’s scale, priorities, and AI maturity.

For example, TensorFlow commands the highest skill premium at 27,104 PLN monthly, indicating strong demand for deep learning expertise. AWS cloud skills also show significant value at 25,896 PLN monthly, reflecting the importance of scalable infrastructure.

Analytics and DevOps skills maintain relevance with SQL appearing in 31 postings and Docker in 16 positions. Version control through Git appears in 10 postings, while Kubernetes container orchestration is mentioned 8 times, showing the integration of modern software development practices.

AI/ML roles requiring 6+ technical skills average 26,662 PLN monthly, compared to 24,452 PLN for standard 3-skill positions.

However, the relationship is non-linear, with diminishing returns beyond 6 skills, suggesting optimal skill breadth for maximum compensation.

Note: One potential issue with our data is that it is likely that there are more skills required which are not marked explicitly within the job posting. For example knowledge of modern software development practices may not be marked but required, same as with knowledge of specific cloud platforms. This may skew some of the data and insights.

Skillset Needs by Company Size

Large Companies: Cloud Infrastructure, Scale, and Governance

At enterprise level, job descriptions consistently include Python, Azure, and Databricks, reflecting the heavy use of cloud infrastructure and enterprise data platforms. Skills such as LLMs, NLP, GenAI, MLOps, and DevOps are frequently paired with TensorFlow, PyTorch, and scikit-learn, indicating that candidates are expected to develop and deploy AI systems at scale.

Additional emphasis is placed on integration tools like SQL, Docker, FastAPI, and orchestration frameworks like IaC (Infrastructure as Code). The presence of LangChain, Azure AI Foundry, and spaCy points to the growing focus on language model governance and retrieval-augmented generation (RAG) techniques in corporate AI deployments.

What this means for applicants: High specialization is rewarded. Candidates with a strong command of cloud-native AI platforms, large-scale model deployment, and cross-functional integration will typically see the highest salary brackets, especially for roles like AI Engineer, AI Architect, or Machine Learning Lead.

Medium-Sized Companies: Full-Stack AI, Platform Familiarity, and MLOps

Medium-sized companies maintain a strong focus on Python, Azure, AWS, and Databricks, but the stack broadens to include Terraform, Kubernetes, and Apache Spark,pointing to a need for full-cycle delivery from data ingestion to model deployment.

A recurring pattern in job postings is the combination of cloud engineering (Azure, GCP, AWS) with tools like Airflow, LangChain, and DBT, and integration frameworks like REST, OpenAPI, and GraphQL. This suggests an operational model where AI professionals are expected to not only build models, but also manage pipelines and APIs.

Additionally, tools like Microsoft Power BI, Tableau, and Snowflake appear more frequently, showing the intersection of AI and BI in mid-size firms. Skills in ML frameworks (TensorFlow, PyTorch) are often accompanied by MLOps, reinforcing the focus on reproducible and scalable AI workflows.

What this means for applicants: Versatility matters. Medium-sized firms look for engineers who can deliver end-to-end AI solutions and collaborate across teams. While the salary range is slightly below large enterprises, the required skill set is almost as broad, particularly for MLOps Engineers, AI Developers, and Data Scientists.

Small Companies: Agility, Innovation, and Emerging Frameworks

Startups and smaller tech firms center their AI hiring around Python, often combined with JavaScript, Node.js, TypeScript, and React, highlighting a need for hybrid skill sets bridging AI and software engineering. These companies heavily use emerging technologies like LangChain, LlamaIndex, OpenAI API, and RAG frameworks, with frequent mentions of FastAPI, Flask, and Pydantic-AI.

There is a noticeable preference for flexible deployment stacks, including Docker, Kubernetes, Apache Airflow, and GCP, but with less emphasis on formal governance or enterprise-grade MLOps. Instead, small firms prioritize speed of development and integration into products, often involving GenAI, ChatGPT variants, and agentic frameworks.

What this means for applicants: Expect fast-paced, innovation-driven environments. Candidates comfortable with rapid experimentation, hands-on deployment, and working across multiple toolchains will thrive. While salaries may vary by funding stage, many small firms offer above-average compensation for specialists in LLMs, LangChain, or generative AI APIs.

Salary Ranges by Role

In the AI field, what you specialize in can significantly affect how much you earn. While most roles share core skills, like Python, data handling, and model development, the nature of the work, its complexity, and its business impact can push salaries in very different directions.

AI Engineer and Machine Learning Engineer: Most Sought Positions

AI Engineer and Machine Learning Engineer emerge as the most common job titles with a total of 143 job posts, >50% of the total. They are followed by Data Scientist with 36 job posts (14% of total) and Python Engineer with 31 job posts (12% of total).

The distribution reflects the market's focus on practical implementation rather than research roles.

Here's how average annual salaries break down across key AI specializations in Poland across seniorities:

  • AI Engineer: 23,078 PLN/month
  • Machine Learning Engineer: 26,638 PLN/month
  • Data Scientist: 21,832 PLN/month
  • Python Developer: 23,944 PLN/month

Role Descriptions

AI Engineer

AI Engineer positions continue to see rapid growth, driven by the widespread adoption of large language models (LLMs), enterprise-grade agents, and AI-powered automation.

Demand is especially strong for professionals skilled in working with foundation models from platforms like Azure OpenAI, Amazon Bedrock, and open-source stacks integrated via LangChain, vector databases, and Retrieval-Augmented Generation (RAG) pipelines.

Mid to senior-level AI Engineers are particularly sought after in sectors like banking, financial services, and insurance. The average salary for these roles is around 23,078 PLN/month, with experienced specialists and those working on production-grade LLM systems often earning well above that range.

Machine Learning Engineer

Machine Learning Engineer is one of the most in-demand roles in the AI space and consistently ranks among the highest-paying. These professionals focus on building, training, and deploying machine learning models in real-world applications.

Proficiency with tools like TensorFlow, Azure Machine Learning Studio, and experience with cloud platforms such as Azure, AWS, or GCP can significantly increase earning potential. According to our research, Machine Learning Engineers in Poland earn an average of 26,638 PLN per month.

Data Scientist

A flexible and widely adopted role, Data Scientists work across industries to extract insights from data, build predictive models, and inform business decisions.

Because the role varies, from analytics-focused to highly specialized machine learning work, so do salaries. On average, experienced Data Scientists earn around 21,832 PLN/month, with top ranges going much higher.

Python Engineer

Python Engineers are the backbone of many AI/ML teams, providing the foundational codebase, infrastructure, and integrations needed for intelligent systems to function. While many AI/ML roles include Python as a required skillset, they are not always marked as "Python Engineers".

Typical responsibilities include developing microservices, maintaining ETL processes, building model-serving infrastructure, and collaborating closely with data scientists or ML engineers. In some organizations, Python developers are also expected to contribute to performance optimization, MLOps automation, and deployment on cloud platforms such as AWS, Azure, or GCP.

Demand is highest in companies prioritizing Python-centric stacks and scalable AI infrastructure. Common tools include FastAPI, Flask, Docker, Git, Kubernetes, and orchestration frameworks like Airflow.

Average compensation for Python Engineers in AI-focused environments sits around 23,944 PLN/month, with those supporting advanced AI/ML systems, including LLM-based applications, often earning more.


Geographic Distribution of Salaries

Where someone works in Poland can make a big difference in what they earn in an AI/ML role. While skills and experience matter most, location still plays a role, mostly because of differences in demand, company presence, and cost of living.

Warsaw clearly leads the way. It has the highest number of job postings and the widest salary range, including some of the top-paying roles in the country. Many large companies and international tech hubs are based here, which helps push salaries up. At the same time, the data shows some below-market offers for mid/regular seniority engineers, especially by small businesses which brings Warsaw’s average to below some other cities. This is not necessarily a nefarious action by those companies, they may be less able to compete against the big players in Warsaw.

Recent job data confirms Warsaw’s position as Poland’s AI capital:

  • Warsaw – 117 job postings
  • Remote – 71 postings
  • Kraków – 26 postings
  • Wrocław – 12 postings
  • Gdańsk – 7 postings

These figures reflect where companies are investing in AI talent, and where job seekers are most likely to find opportunity.

Remote roles are also very common. In fact, they make up a large share of all AI job listings, sitting right behind Warsaw. What’s interesting is that remote positions often offer salaries on par with or even higher than those based in smaller cities, especially for senior roles or hard-to-find specializations.

Kraków and Wrocław also show up regularly on the AI job map. They are mature tech hubs which are still growing. Salaries here as well as other cities other than Warsaw tend to be more consistent across roles, with a lower number of outliers.

Gdańsk has fewer listings and the tightest salary range among the major cities. It’s still an active tech hub, but the AI job market there is smaller, even though the pay levels are on par with other major cities.

Here’s a quick summary of average salaries by location:

Location and Average Salary (PLN per month)

  • Warsaw: 24,027 PLN/month
  • Krakow: 24,906 PLN/month
  • Wroclaw: 26,098 PLN/month
  • Gdansk: 25,091 PLN/month

While Wrocław currently shows the highest average salary, the difference between top cities is relatively small, by a margin of ~2%. These differences reflect local employer concentration, industry maturity, and regional demand.

Why Location Still Matters

Even as companies embrace remote hiring, geography continues to impact pay, especially for mid-to-senior roles. Here's why:

  • Cost of Living: Larger cities offer higher pay to offset housing and living expenses.
  • Industry Presence: Cities with international R&D centers and tech hubs, such as Kraków, Warsaw, and Wrocław host more AI-focused roles and higher-paying employers.
  • Access to Career Growth: Urban areas provide more diverse projects and leadership roles, pushing salaries upward over time.
  • Remote Work Levels the Field, But Not Completely: While B2B remote contracts may match or exceed local offers, the location of the hiring company still influences salary bands.

Comparing to Other Tech Areas

The average AI/ML position offers 24,495 PLN per month, representing a premium of 1,213 PLN over the average of other tech areas. This salary advantage varies across seniority levels, with the greatest difference observed at Mid/Regular levels.

Salary Distribution Analysis

The comprehensive salary analysis reveals notable differences in compensation across tech areas. AI/ML positions show not only higher average salaries but also a wider range, suggesting greater variability in compensation packages.

The median salary for AI/ML jobs is 24,000 PLN per month, compared to 25,100 PLN for Data roles, 23,510 PLN for Backend positions, and 20,000 PLN for Frontend roles. While Data positions show a higher median salary, AI/ML positions offer higher maximum salaries, with top positions reaching 62,400 PLN per month.

The distribution of salaries reveals that AI/ML positions tend to cluster at higher salary ranges (24,000-30,000 PLN), while Frontend positions show more concentration in lower ranges. Backend and Data positions show similar distributions but with different spread patterns.

AI/ML Salary Premium

AI/ML positions command a significant premium over other tech areas, particularly compared to Frontend roles where the difference reaches 2,880 PLN per month on average. The premium over Backend positions averages 1,219 PLN, while the advantage over Data roles is more modest at 688 PLN per month.

This salary advantage reflects the specialized skills required for AI/ML roles, including expertise in areas such as machine learning algorithms, neural networks, natural language processing, and computer vision.

Seniority Impact on Salaries

Seniority level has a significant impact on compensation across all tech areas, with a clear progression from Mid/Regular to Manager positions. Average salaries range from 21,705 PLN for Mid/Regular roles to 29,445 PLN for Manager positions.

The data shows that AI/ML maintains its salary advantage across most seniority levels, with the premium being most pronounced at the Mid/Regular and Senior levels. At the Lead/Principal and Manager levels, the salary differences between tech areas tend to narrow, suggesting that leadership skills become more important than technical specialization at these levels.

The greatest number of job postings (359 positions, 58.1% of total) are for Mid/Regular roles, followed by Senior positions (194 jobs, 31.4%), indicating that companies are primarily seeking experienced professionals rather than entry-level talent.

Skillset Requirements by Technology Area

The analysis shows that most tech positions across all areas require approximately 4 skillsets, but with notable variations in range and consistency.

All four major technology areas—AI/ML, Backend, Frontend, and Data converge around this 4-skillset standard as their most common requirement.

Specialization and Area-Specific Requirements

AI/ML positions demonstrate the highest complexity and variability in skillset requirements, with needs ranging from 1 to 10 skillsets (mean: 4.12, standard deviation: 2.05).

This extensive range reflects the diverse nature of AI/ML roles, from specialized research positions to comprehensive full-stack AI engineering roles, and the non-consolidated market where there are many tools and skills needed which are changing month-to-month.

Backend development: maintains relatively consistent requirements, typically demanding 3-5 skillsets with a mean of 4.05 and low variability (standard deviation: 0.39). This consistency suggests well-established patterns in backend development practices.

Frontend development: shows the most uniform requirements, with skillset counts ranging only from 3-4 (mean: 3.97, standard deviation: 0.16). This narrow range indicates standardized expectations for frontend developers.

Data roles: exhibit similar uniformity to Frontend positions, requiring 3-4 skillsets (mean: 3.98, standard deviation: 0.13).

Why AI/ML Exhibits Greater Skill Diversity

The Meta-Discipline Effect

AI/ML functions as a meta-discipline that uniquely bridges multiple technical domains. Unlike traditional areas that represent specialized technical stacks, AI/ML combines elements from computer science, mathematics and statistics, domain expertise, infrastructure, and data engineering.

Technology Evolution and Framework Competition

The rapid evolution of AI/ML technologies contributes significantly to skill diversity. Emerging technologies like Large Language Models (5.8% prevalence) and Generative AI (3.3%) represent cutting-edge requirements.

Additionally, the coexistence of competing frameworks (TensorFlow vs PyTorch both appearing in significant percentages) and cloud-agnostic requirements (both AWS and Azure needed) creates natural skill diversification.

Project Diversity Impact

AI/ML projects demonstrate exceptional variety in technical requirements, spanning computer vision, natural language processing, traditional machine learning, research versus production deployment, real-time versus batch processing, and different data types and scales. This project diversity necessitates correspondingly diverse skill requirements.

Technology-Specific Skill Demands

AI/ML Skills

Python dominates the AI/ML skillset requirements, appearing in >66% of all skill mentions within the area. The "AI" skill itself represents 6.6% of mentions, followed by SQL (3.9%), AWS (3.5%), and Azure (2.6%).

Backend Development Skills

Java leads backend requirements at 15.2% of skill mentions, with Python following at 10.5%. The .NET framework represents 7.9% of mentions, while PHP accounts for 3.2%.

Frontend Development Skills

React dominates frontend requirements with 27.1% of skill mentions, significantly higher than other areas' leading skills. Angular follows at 14.2%, with JavaScript at 13.5%, TypeScript at 7.1%, and HTML at 5.8%.

Key Findings and Implications

  1. Standardization vs. Specialization: While most positions require 4 skillsets, AI/ML roles show significantly more variation, reflecting the field's dynamic nature and diverse application areas.
  2. Skill Quantity vs. Compensation: More required skillsets do not correlate with higher salaries, suggesting that role seniority, specialization, and market demand have greater salary impact than skill breadth.
  3. Technology Concentration: Each area shows clear skill concentration patterns—Python in AI/ML, Java in Backend, and React in Frontend—indicating core competencies that drive hiring decisions.
  4. Market Maturity: The low variability in Frontend and Data roles suggests mature markets with established skill requirements, while AI/ML's high variability indicates a rapidly evolving field.

The analysis demonstrates that while skillset requirements follow predictable patterns across technology areas, their relationship to compensation is more complex than simple skill counting might suggest.

Organizations and professionals should consider role context, market positioning, and specialization depth alongside skill breadth when evaluating positions and compensation expectations.


Salary Ranges by Company Size

AI salaries in Poland show noticeable variation depending on company size, largely driven by differences in funding, project scope, international exposure, contract flexibility, and organizational maturity. Here is how AI job compensation typically compares across small, medium, and large companies.

Small Companies

Salary Range: 21,730–28,209 PLN/month

Startups and boutique tech firms tend to offer competitive compensation, especially for AI-focused roles that demand advanced skills. Many of these companies rely on B2B contracts, which allow them to offer higher take-home net salaries despite having smaller budgets.

Additionally, small companies often work on cutting-edge or niche AI use cases, which can appeal to candidates looking for impact and technical challenges. However, they may not offer extensive benefits packages or long-term employment security.

Medium-Sized Companies

Salary Range: 21,763–27,460 PLN/month

Mid-sized firms often strike a balance between competitive salaries and organizational stability. These companies are typically scaling rapidly or operating in specialized verticals like technology consulting, financial services, or healthcare.

While salaries may be slightly lower than those in some startups at the top end, benefits such as career development programs, partial remote flexibility, and structured teams make these roles appealing. Their compensation is often tied to clear technical ladders and project responsibility levels.

Large Companies

Salary Range: 22,391–28,737 PLN/month

Large multinational corporations and enterprises usually offer the highest average salaries, especially for senior-level or highly specialized AI roles. Their compensation structures reflect both their financial capacity and global standards.

These firms are more likely to hire under employment contracts (UoP) and may offer additional benefits like stock options, and wider health insurance coverage. While they may be slower to adopt emerging technologies compared to startups, they provide structured career paths, larger-scale AI applications, and access to international teams.

Why Do AI Salaries Differ by Company Size?

  • Contract Types: Small and medium companies are more flexible with B2B contracts, which often result in higher take-home pay despite lower gross compensation.
  • Funding & Resources: Larger companies have greater capital and can afford to pay more for top-tier talent, while small firms focus on lean teams and may compensate with flexibility and technical ownership.
  • Project Scope: Large firms often deploy enterprise-scale AI systems that require seasoned professionals, justifying higher pay. Startups, meanwhile, may offer more innovation-driven roles but with fewer operational layers.
  • Benefits vs. Flexibility: Large companies lead on structured benefits, while smaller firms typically provide more autonomy and remote options.

Future Outlook

The AI job market in Poland is showing no signs of slowing down. In fact, all signs point toward continued growth, both in the number of roles and in the range of specializations emerging across the industry.

  • Continued growth in AI job opportunities: As more companies integrate AI into their products, operations, and decision-making processes, the demand for AI talent is set to rise further.

    This applies not only to large corporations but also to mid-sized firms and startups that are beginning to adopt AI at scale. Sectors like finance, healthcare, retail, and logistics are especially active, with new roles opening in areas such as automation, personalization, fraud detection, and AI-driven analytics.
  • Potential increase in salary ranges: With more competition for skilled professionals, and the expansion of remote hiring, salaries are likely to climb, especially for mid and senior-level roles.

    As the market matures, companies are also becoming more aware of the value AI professionals bring, leading to better compensation packages that include performance bonuses, equity, and flexible work options.
  • Emergence of new AI specializations: The field is evolving quickly. While machine learning and data science are still at the core, new roles are emerging around areas like model governance, responsible AI, AI operations (AIOps), and large language models.

    There’s also growing demand for professionals who can bridge AI and business, such as AI product managers or technical consultants. These specializations will likely shape the next wave of salary benchmarks and job expectations.

Overall, the outlook is positive. The combination of local talent, international investment, and evolving technology means Poland’s AI job market is not only growing, it’s becoming more diverse, better paid, and more globally connected.


Considerations for Employers

Hiring AI talent in Poland is increasingly competitive, and not just among local companies, but also against international firms hiring remotely or setting up local hubs. Here are practical considerations for employers who want to attract and retain strong candidates in this environment:

  • Benchmark locally, and globally when setting salaries - Top AI talent in Poland often receives offers from companies in Germany, the Netherlands, the UK, and even the US (via remote roles).

    Offering salaries that only reflect the local cost of living may no longer be enough. Use salary data from both local competitors and global players hiring in Poland to calibrate your offers, especially for mid- and senior-level roles. Underpaying means losing candidates before the first interview.
  • Invest in role clarity and long-term career paths - Many candidates are cautious of “AI” roles that turn out to be data cleaning or dashboard maintenance.

    To attract the right talent, clearly define whether a role is R&D-focused, production engineering, or applied analytics. Then offer a roadmap: will this person eventually lead a team? Own an AI product line? Work with LLMs? Candidates are looking beyond the first 6 months.
  • Create hands-on learning opportunities, not just online courses - While it’s common to offer LinkedIn Learning or Coursera access, top candidates care more about on-the-job development.

    Let them rotate across teams, work on real-world use cases, experiment with new tools like LangChain, vector databases, or generative AI models. Internal hackathons, POCs, or time to work on self-proposed ideas (like Google’s 20% time) signal a serious commitment to learning.
  • Highlight real innovation, not buzzwords - Many job ads in Poland mention AI, but few actually offer meaningful AI work. Candidates can spot this quickly.

    If your teams are working on interesting challenges, say, building LLM-based document processing, automating MLOps pipelines, or applying computer vision in manufacturing, talk about it openly. Real innovation attracts top-tier applicants far more than generic job titles.
  • Support hybrid and remote options, but be clear - Remote and hybrid flexibility remains a key factor. If you offer this, define how it works in practice: how many days in-office? Are standups timezone-friendly? Will remote employees have equal access to promotions? Candidates want flexibility, but also clarity.

Recommendations for Job Seekers

The AI job market in Poland is growing, but so is the competition. To stand out, it's not enough to just meet the job requirements. The most successful candidates are the ones who can clearly show what they’ve built, how they think, and how they stay current in a fast-moving field. Here are a few ways to stay ahead:

  • Develop a strong portfolio of projects: Nothing speaks louder than hands-on experience. Whether it’s a personal project, open-source contribution, or something built during a course, a clear and well-documented portfolio goes a long way. Projects that demonstrate end-to-end thinking, data collection, model development, deployment, are especially valuable. Hosting projects on GitHub and sharing results (e.g., via notebooks or dashboards) can set you apart.
  • Stay updated with the latest AI technologies: The field is evolving rapidly. Tools, frameworks, and best practices change every year. Following updates from platforms like Hugging Face, OpenAI, or Google AI, and experimenting with new models (e.g., GPT, Claude, Gemini) can help you stay relevant. Learning how to work with large language models, prompt engineering, or vector databases is becoming increasingly important.
  • Network with industry professionals: Many opportunities come through connections, not job boards. Join AI meetups, conferences, hackathons, or online communities like Data Science PL or AI in Poland. Sharing knowledge and learning from others not only builds your visibility, it often leads to job leads, mentorship, and collaboration.
  • Consider continuous learning and certifications: While not always required, certifications can help validate your expertise, especially in areas like cloud (AWS, Azure, GCP), MLOps, or data engineering. Online platforms like Coursera, DeepLearning.AI, DataCamp, and Udacity offer high-quality, recognized programs that can fill knowledge gaps or show commitment to the field.

Bonus tips

  • Tailor your CV to match the language of the job posting. This matters in ATS (applicant tracking systems).
  • Include measurable impact in your experience (e.g., “Improved model accuracy by 15%”).
  • If you're early in your career, don't overlook internships or research assistant roles. They’re a strong stepping stone in this space.

Final thoughts and notes

Distribution Overview

The salary distribution reveals a near-normal curve with a slight right skew, indicating a healthy and mature job market for AI/ML professionals in Poland. While salaries are distributed across a wide range from 8,000 PLN to 64,000 PLN per month, most positions concentrated in the mid 20,000-30,000 PLN/month salary range.

Key Distribution Findings

Peak Salary Range

The highest concentration of AI/ML jobs falls in the 24k-28k PLN range, representing 15.0% of all available roles. This peak suggests that most companies are offering competitive mid-level salaries that align with market standards for AI/ML professionals.

Market Concentration

Approximately two-thirds (67%) of all AI/ML positions fall within the 20k-36k PLN salary range, indicating strong market consensus around competitive compensation levels. The weighted average salary across all positions is 31,117 PLN per month.

Further Research Needed

While this report outlines key trends and benchmarks, several areas still need deeper exploration to get a more complete and reliable picture of AI compensation in Poland.

  • Specific salary data from surveys - A major challenge in understanding the AI job market is the lack of consistent, publicly available salary data. Many job postings omit compensation ranges altogether, and company-provided figures are often outdated or incomplete. Anonymous salary surveys, especially those broken down by role, city, and experience level, would offer clearer, first-hand insights into what professionals are actually earning.
  • Detailed analysis of industry-specific variations - Salaries can vary widely depending on the sector. AI roles in banking, insurance, healthcare, or ecommerce often come with very different expectations, project scopes, and compensation levels. A deeper industry-by-industry breakdown would help both job seekers and employers benchmark more effectively within their specific context.
  • Impact of remote work on salaries - Remote and hybrid work has shifted salary expectations, especially for candidates working for international companies from within Poland. However, it’s still unclear how remote roles compare to local in-office offers in the long run. Future research should explore whether remote flexibility is driving salaries up, creating gaps between local and global employers, or reshaping career expectations for AI talent.
  • Regional and emerging city benchmarks - Most available data focuses on Warsaw and a few major cities. Yet, there’s growing AI activity in smaller tech hubs like Poznań, Łódź, Lublin, and Rzeszów. These regions are often overlooked, but they could offer more affordable hiring options, or growing demand that shifts salary baselines in the next few years.
  • Career trajectory and progression trends - Another area worth investigating is how long it typically takes for AI professionals in Poland to move from junior to mid-level or senior roles. Mapping out the average salary progression across experience levels would help set realistic expectations for both candidates and employers.
Thank you and we hope the report was useful!
Maciej Gos
Chief Architect
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Hisham Itani
Head of Marketing
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