Top Machine Learning Jobs in Poland

Own the strategy, roadmap, metrics, and adoption of JetBrains Context, a managed retrieval layer for AI coding agents. Partner with engineering, ML, platform, IDE, marketing, sales, and customer success teams to improve indexing, retrieval quality, integrations, reliability, cost, and latency. Define product direction, evaluate competitors, establish quality metrics, communicate with customers and developer communities, and drive enterprise adoption.

Own the Vault product roadmap and lifecycle for a sports media asset platform. Lead discovery with customers, define solutions for content ingest, storage, integrations, AI enrichment, metadata, search, rights, and asset monetization. Partner closely with engineering, sales, and customer success, attend customer events, and prioritize product bets that help sports organizations maximize the value of extensive media archives.

Corti is seeking passionate individuals to join their talent community, shaping the future of healthcare through AI technologies. Opportunities include roles in AI technology, machine learning, and software development, focusing on reducing errors and improving patient outcomes.

Designs and operates scalable production data infrastructure for high-volume athlete, sensor, and time-series data. Builds streaming ingestion, graph models, low-latency ML feature systems, evaluation and monitoring infrastructure, and multi-tenant architectures. Ensures data provenance, lineage, observability, reliability, and correctness while collaborating with ML engineers, data scientists, sport scientists, and domain experts. Requires expertise in Python, SQL, streaming technologies, AWS, graph data modeling, causal inference, AI evaluation, and telemetry systems.

Develop, validate, deploy, and maintain machine learning solutions for medical device manufacturing and CAD/CAM systems. Build deep learning and multimodal AI models using computer vision, 3D geometry, meshes, images, and structured data. Integrate models into production systems, conduct performance analysis, maintain ML workflows, and translate research into practical manufacturing applications while collaborating with engineering, manufacturing, and product teams.

Develops analytical and machine learning solutions for finance risk and control processes. Responsibilities include data governance, ERP data extraction, reporting, Power BI development, technical environment maintenance, risk analytics, and translating business needs into digital solutions. The role partners with Risk Managers, ERP teams, Process Operations, and Global Process Owners to improve controls, identify process risks, and deliver insights. It requires strong hands-on expertise in Azure, SAP, SQL, Python, data engineering, and financial risk management.

Build and deploy production machine learning systems for classification, retrieval, ranking, personalization, and evaluation. Develop models, datasets, metrics, and training, inference, and deployment pipelines. Optimize accuracy, relevance, latency, cost, and reliability while monitoring production performance and incorporating user feedback. The role involves debugging ML systems, fine-tuning existing models, and collaborating with engineering and product teams to deliver reliable AI-powered features.

Own the data foundation for Operations, including BigQuery data flows, analytical models, automated KPI dashboards, and Tableau reporting. Analyze payments, support, online reputation, funnels, churn, lifetime value, fraud, and operational bottlenecks. Partner with Operations leaders to convert insights into process improvements and business growth, while implementing AI and LLM automation for support classification, routing, and fraud detection.

Set architecture and modeling standards for credit-scoring and merchant-underwriting ML systems. Own the full model lifecycle, including feature engineering, validation, champion/challenger testing, deployment, monitoring, explainability, responsible AI, and regulatory documentation. Design agentic ML iteration and monitoring workflows, establish reusable engineering patterns, guide feature-store strategy, review designs and code, and mentor ML engineers.

Build and operate credit-scoring and merchant-underwriting ML models across feature engineering, training, validation, deployment, monitoring, and retraining. Develop point-in-time-correct features, MLOps pipelines, agentic model lifecycle and validation loops, explainable models, backtesting harnesses, and audit-ready evidence for fintech regulatory requirements.

Design and deploy multi-turn conversational AI systems, agentic workflows, and LLM-powered solutions for complex enterprise domains. Lead AI initiatives, mentor junior staff, promote engineering best practices, and collaborate with customers, engineers, and product managers. Build, benchmark, and maintain end-to-end models using Python, Azure, MLflow, and Databricks while evaluating emerging generative AI technologies.

Design, deploy, and maintain scalable AI/ML systems and infrastructure. Build MLOps, LLMOps, and AgentOps pipelines; orchestrate batch and real-time workflows; run experiments; monitor models; and integrate AI solutions into business applications. The role uses Python, MLflow, Azure services, and Databricks, while collaborating with scientists, engineers, and product teams to deliver production-grade AI/ML products.

Optimize large-scale ML inference systems for throughput, latency, cold-start time, and cost per token. Tune SGLang, vLLM, and TensorRT-LLM across GPUs and distributed clusters; improve batching, scheduling, KV caching, quantization, weight loading, JIT caching, and multi-node inference. Profile and instrument systems to validate performance gains, eliminate storage and networking bottlenecks, and establish technical direction through reproducible benchmarks and experiments.

Design, build, and deploy agentic AI, LLM, computer vision, and machine learning systems from data pipelines through production. Develop multi-agent workflows, RAG pipelines, tool integrations, model training, MLOps, evaluation, monitoring, and APIs. Collaborate with architects, product teams, engineers, and business stakeholders to deliver reliable AI solutions with measurable operational impact across cloud and edge environments.

Build and operate production AI systems, including LLM agents, multimodal applications, RAG pipelines, classical ML models, and AI services. Own solutions from requirements and prototyping through deployment, monitoring, evaluation, and support. Develop Python services on Azure using PostgreSQL, Terraform, containers, CI/CD, and observability tools. Integrate AI with engineering, manufacturing, testing, and business systems while improving technical architecture, mentoring engineers, and guiding responsible AI adoption.

Build and deploy production ML systems for large-scale ecommerce search and recommendations using text, image, and multimodal approaches. Tune and integrate LLMs and transformer models, develop end-to-end visual solutions, improve business KPIs through experimentation, and collaborate across teams to productize and scale ML capabilities.

Build and maintain Ergodic AI’s AI platform across back-end and front-end systems. The role involves developing with Python web frameworks and JavaScript/TypeScript, integrating with client technology stacks and workflows, applying AI, ML, and LLM concepts, and contributing to DevOps, cloud deployment, testing, and infrastructure practices. As an early-stage hire, the engineer will help shape the product and solve complex data and AI challenges.

Develop AI-augmented blockchain risk and threat intelligence systems, including autonomous monitoring and investigation workflows. Lead forensic investigations into crypto theft, fraud, and market manipulation. Research post-quantum cryptography and quantum threats to blockchain security, evaluate migration paths, and collaborate with internal teams, external stakeholders, and academic institutions to deploy emerging security tools.

Develop and maintain platforms for deploying banking fraud-detection machine learning models. Responsibilities include code and compute optimization, unit testing, distributed scheduling, shared model-development codebases, model deployment support, platform maintenance, and collaboration with data scientists. The role uses cloud, data engineering, distributed computing, and MLOps technologies to optimize models across ING’s international banking operations.

Designs, develops, evaluates, and deploys AI, machine learning, GenAI, and agentic AI solutions. Translates business needs into scalable production capabilities using LLMs, RAG, semantic search, enterprise data, APIs, and intelligent workflows. Partners with business, analytics, and technology teams to move proofs of concept into reusable solutions while applying software engineering, MLOps, LLMOps, security, governance, monitoring, and documentation best practices.

Supports Account Executives throughout the sales cycle by leading technical discovery, solution design, demonstrations, workshops, proofs of concept, evaluations, and technical validation. Translates engineering, simulation, HPC, AI, and infrastructure requirements into Rescale recommendations, documents opportunities in Salesforce, resolves technical risks, supports negotiations, and ensures effective post-sale handoffs across Northern Europe.

Leads SGS’s enterprise AI agenda for Finance, managing strategy, roadmap, use-case prioritization, governance, cross-functional delivery, responsible AI controls, adoption, and value realization. Partners with Finance, IT, Data, Risk, Cybersecurity, Privacy, Legal, and external vendors to industrialize AI solutions, improve business outcomes, ensure compliance, and report portfolio performance to senior stakeholders.

Perform multilingual legal data collection, evaluation, and annotation for AI-powered contract management platforms. Analyze clauses, obligations, and terms in international business agreements, participate in onboarding and calibration sessions, conduct quality reviews, and collaborate within secure client environments. Requires legal or contract expertise and fluency in English and Polish. Prior data annotation or machine learning evaluation experience is preferred.

Build and enhance RAG systems, AI components, backend services, data pipelines, and REST APIs under senior guidance. Write production code, tests, evaluation harnesses, documentation, and runbooks. Support AWS deployments using containers and CI/CD, investigate model failures, and contribute to architecture and client handovers. The role offers increasing ownership of technical components and decisions while working in distributed, customer-facing delivery teams.

Design and optimize ML models for perception, prediction, and planning in automotive parking systems. Build computer-vision and multi-sensor processing solutions, trajectory forecasting, training/evaluation pipelines, analyze large datasets, validate models in real-world scenarios, and collaborate with software, systems, and product teams to improve algorithms and tools.