AI / ML / Data Science: market
AI / ML / Data Science is a family of roles: ML Engineer (production models and pipelines), Data Scientist (statistical analysis, A/B tests, ML models), AI Engineer (LLM, RAG, GenAI applications), MLOps (ML infrastructure), Computer Vision and NLP engineers. The core stack is Python, PyTorch, TensorFlow, scikit-learn, pandas, NumPy, Jupyter, MLflow, Kubeflow, HuggingFace Transformers, LangChain, OpenAI API, vector databases (Pinecone, Weaviate, Qdrant). The most in-demand technologies — python, cloud, sql, llm, pytorch. 27% of positions are remote. Data refreshes daily.
The AI / ML / Data Science market currently has 9,926 open roles, 9,383 of them freshly observed. Median salary $11,250/mo.
27% of AI/ML jobs are full-remote; the rest are hybrid or office. Banks more often require hybrid because of compliance; international AI teams — full-remote is the standard.
⚠ salary known for 513 of 9926 jobs; remote share from 8518 with a stated format; 9383 counted as fresh observations; trend and hiring difficulty are not shown
Demand and observed supply
| Open demand | 9,926 |
| Observed supply | — not published |
Over recent weeks the AI/ML direction has grown non-linearly — the explosive rise in demand since the releases of ChatGPT, Claude, Gemini in 2023–2025 continues in 2026. Fluctuations are normal; watch the overall trend.
⚠ candidates matching a vacancy are not counted yet: demand and the observed pool are shown
Salary distribution
One in ten earns under $4,360, one in ten over $20,833. Half the market falls between $6,847 and $15,937. Sample: 513.
Demand geography
| country | jobs |
|---|---|
| US | 3,959 |
| GB | 910 |
| IN | 402 |
| SG | 401 |
| DE | 379 |
| PL | 317 |
| CA | 291 |
| FR | 201 |
| HK | 154 |
| RU | 108 |
The leader by AI/ML job count is US (3959 positions), followed by the major IT hubs of CIS and Eastern Europe. Important: this is the distribution across our parsing sources, not a global market estimate.
⚠ job counts only: salary by country is not published
Used together with
The top ML/AI engineer stack in 2026 is Python, PyTorch, TensorFlow, scikit-learn, pandas. PyTorch dominates in research and AI startups; TensorFlow is a solid enterprise choice. HuggingFace Transformers and LangChain are must-have for AI Engineer.
The most common technology pairs in AI/ML postings: Python+PyTorch, Python+TensorFlow, scikit-learn+pandas, HuggingFace+LangChain, MLflow+Airflow. If you are planning a learning roadmap, these combinations maximise market coverage.
Demand by grade
| grade | jobs |
|---|---|
| senior | 2,880 |
| principal | 1,093 |
| lead | 877 |
| junior | 214 |
| middle | 186 |
The strongest pay growth is between Middle and Senior (picking up production-ML and MLOps).
⚠ demand side only: the grade of the observed pool is unknown for most of it
Employers
Demand is spread across 3,727 employers. The largest accounts for 1.6%, the top ten for 9.9%; the remaining 90.1% is long tail.
| share | value |
|---|---|
| top-1 | 1.6% |
| top-3 | 4.2% |
| top-10 | 9.9% |
| long tail | 90.1% |
The companies hiring ML/AI engineers most actively right now are large banks, marketplaces, search engines, and international AI startups. Only those with 3+ open ML/AI positions are shown.
⚠ names are not shown: staffing agencies and end employers are not yet told apart by the classifier
Where Zorky sees this market
Observed across 157 sources; the largest accounts for 28.2% — this market does not rest on a single channel.
AI/ML jobs surface across most major sources: web parsers (HH, Habr Career, Djinni, DOU, NoFluffJobs, JustJoin.it) provide the bulk of the volume.
Recent openings
- Senior ML Engineer · PL
- IT Production Analyst / Specialist (Major Bank) · HK
- Finance SME – Chemicals / Petrochemicals (Downstream) · SA
- Finance SME – Power & Utilities (Downstream) · SA
- Finance SME – Trading (Crude, Products & Commodities) (downstream) · SA
- Finance SME – Refining (Downstream) · SA
- Senior Data Scientist - MMM · GB
- Data Scientist (РСХБ-Экосистема) · RU
Latest open AI/ML/DS jobs — the most recent 10 positions with adequate description quality. The full list is available in our CRM or via the "see all" link below.
Adjacent markets
AI/ML/DS is the highest-paid IT specialisation by median in 2026. Click any direction's bar to compare salaries, stack, and dynamics in detail.
⚠ adjacent markets for comparison are not defined yet
How this is measured
- Vacancy
- an open job that cleared the quality gate and lists at least two technologies
- Observed candidate
- a candidate whose stack contains this technology; an aggregate — not a single record leaves the perimeter
- Matchable candidate
- not counted yet
- Window
- jobs open at the moment the snapshot was built
About the data
- Some breakdowns are hidden: their data coverage is not yet sufficient.
- Statistics are shown only where the sample clears a quality gate.
- A missing block does not mean a value of zero.
Breakdowns currently hidden: 8.
Data as of 2026-10-20
Specializations
AI/ML/DS breaks down into 7 sub-specialisations: ML Engineer (production models), AI/LLM Engineer (generative AI), MLOps (ML infrastructure), Computer Vision (images and video), NLP Engineer (text and language), Research Engineer (R&D), Data Scientist (statistics and A/B tests). Each niche has its own salary range — click a card for detail.
Frequently asked questions
The most common questions about the AI/ML/DS market: salaries by level, stack (PyTorch vs TensorFlow), ML Engineer vs Data Scientist vs AI Engineer, remote, where to start a career, Senior skills. Answers recompute automatically from current data.
What stack is most often required of an ML engineer?
Top-5 technologies in AI/ML/DS postings: python, cloud, sql, llm, pytorch. Required baseline skills: Python (advanced), statistics and linear algebra, classical ML via scikit-learn. Deep Learning — one of PyTorch (dominant in research) or TensorFlow (more production-friendly). Data work — pandas, NumPy, Jupyter. SQL is mandatory. For MLOps: MLflow, Kubeflow, Airflow, Docker, Kubernetes. A cloud (AWS SageMaker, GCP Vertex AI) is required at Senior.
Who earns more — ML Engineer, Data Scientist, or AI Engineer?
Demand is growing explosively since the releases of ChatGPT, Claude, and Gemini. Research Engineer (R&D, papers at NeurIPS/ICML) — a high ceiling in international labs.
Can ML engineers work remotely?
Partially: 27% of ML/AI jobs are full-remote. Banks and fintech often require hybrid because of compliance on work with PII and financial data. Marketplaces — more often hybrid. International AI teams — almost always remote-friendly or hybrid with an office in SF/NYC/London. AI startups — full-remote is the standard. Remote pay is markedly higher thanks to the global candidate pool and the premium GenAI segment. Few Junior remote openings exist — production training requires mentorship and access to compute resources.
How is ML Engineer different from Data Scientist and Data Engineer?
ML Engineer — production focus: designing, training, deploying, and monitoring ML models. Stack: Python, PyTorch/TensorFlow, MLflow, Docker, Kubernetes. Close to a Backend engineer with an ML specialisation. Data Scientist — research focus: statistics, A/B tests, hypotheses, dashboards, sometimes ML models. Stack: Python, scikit-learn, Jupyter, pandas, SQL, sometimes R. Data Engineer — data infrastructure: ETL pipelines, warehouses, streams. Stack: Spark, Airflow, Kafka, ClickHouse. By pay: ML Engineer and AI Engineer usually pay above Data Scientist (more technical); Data Engineer is close to ML. Career flow: Backend → ML Engineer, Analyst → Data Scientist, Data Engineer ↔ ML Engineer.
Where to start to become an ML engineer in 2026?
The optimal path: master Python (advanced), statistics and linear algebra at university level, SQL, pandas + NumPy for data work. Then scikit-learn (classical ML: regression, classification, clustering) and one of PyTorch (recommended for research) or TensorFlow. Pet projects that look good: an end-to-end ML project with production deployment (not just a Jupyter notebook), 1–2 Kaggle competitions with silver medal or higher, contributions to open-source ML libraries. At Middle add MLOps (MLflow, Airflow), one cloud (AWS SageMaker or GCP Vertex AI). At Senior — a deep understanding of distributed training, model serving, A/B tests in production.
How many ML/AI jobs are open across CIS and Europe?
As of the latest data refresh, the Zorky CRM sample contains 11809 active open AI/ML/DS positions across CIS and Eastern Europe. These are postings published in the last 90 days — companies actually hiring. Geography is distributed; the leaders are US, GB, IN. Data is collected from 101 job boards and 15 780 company career sites: Telegram channels (an exclusive stream for AI startups and niche LLM positions), specialised job sites (HH, Habr Career, Djinni, DOU, NoFluffJobs, JustJoin.it, Pracuj.pl), and career pages of fintech and AI teams. Seasonality: ML/AI demand grows non-linearly — an explosive rise since 2023 after ChatGPT, continuing in 2026 with releases of Claude, Gemini, Llama.
Where do ML engineers earn more — in Russia or in Europe?
The main driver of the gap is contract currency and company type. Local Russian companies on rouble contracts have closed the gap to the Polish market over 2 years. Georgia and Armenia attract many remote relocants on international pay.
What skills does a Senior ML engineer need in 2026?
A Senior ML/AI engineer owns the full production-ML stack. Baseline: Python (advanced), statistics and linear algebra, SQL, pandas, NumPy. Deep Learning: PyTorch or TensorFlow (one deeply). Production: MLflow or Weights & Biases (experiments), Airflow or Kubeflow (pipelines), one cloud ML (SageMaker, Vertex AI). Architectural patterns: model serving (Triton, TorchServe), A/B tests in production, distributed training, model versioning.
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