Top Machine Learning Jobs in Poland

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.

Manage relationships with AI-native startups, scaleups, and research labs; understand their technical and business needs; develop high-value NVIDIA solutions; drive adoption of AI platforms; coordinate with solution architects, engineering, business development, and go-to-market teams; cultivate ecosystem partnerships, customer references, case studies, and emerging use cases; and represent NVIDIA at industry events.

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.

Review and annotate images by identifying people, actions, and predefined categories. Follow detailed annotation guidelines, maintain accuracy and consistency, review completed work, and help create high-quality datasets for AI models. The role requires strong attention to detail, independent work, reliability, and EU citizenship with residence in the European Union.

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.

Lead the Agentic Discovery product chapter, initially owning business tooling, dashboards, transparency, and controls for AI-powered commerce experiences. Define strategy, align ML, engineering, design, and go-to-market teams, scale experimentation, launch reliable LLM-powered experiences, and establish metrics tied to usage, conversion, revenue, and engagement. The role will expand to chapter-wide leadership across on-site, offsite, and integrated agentic discovery surfaces.

Develop and improve quantitative supply-and-demand models and forecasts for power markets, integrate weather and climate data, apply machine learning and AI methods, process large time-series and geospatial datasets, produce analysis and reports for market participants, and collaborate across research, data science, engineering and product teams.

Designs and deploys production AI/ML solutions and agentic workflows, translating ambiguous business problems into modeling approaches. Builds GenAI evaluation frameworks, golden datasets, testing harnesses, and MLOps/LLMOps pipelines with observability. Implements governance, security, fairness, privacy, and explainability controls while partnering with product, engineering, and business stakeholders on AI adoption, impact, and cost optimization.

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.

Develop and maintain high-load, real-time personalization services for global e-commerce platforms. Improve shopper experiences and business KPIs through applied machine learning, user interaction modeling, personalization and recommender systems, hypothesis validation, and A/B testing. Collaborate with engineering teams and business stakeholders to research, deliver, and communicate personalization capabilities and outcomes.

Design, deploy, and optimize machine learning models and infrastructure using Databricks. Lead end-to-end model development, including data preparation, deployment, monitoring, and production scaling. Collaborate with data scientists, engineers, business stakeholders, and clients to deliver technical solutions. Mentor junior team members and apply advances in machine learning, NLP, computer vision, and MLOps to solve complex business problems.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Own the full enterprise customer journey from kickoff through adoption and ongoing value realization. Build executive and technical stakeholder relationships, lead QBRs and EBRs, translate product performance into business outcomes, manage risks and escalations, provide strategic ecommerce recommendations, maintain success plans, and partner with Account Management on retention and expansion.

Own enterprise customer relationships from kickoff through adoption and ongoing value realization. Lead success plans, QBRs, executive conversations, escalations, stakeholder alignment, training, and strategic recommendations. Translate technical capabilities and performance data into business outcomes such as revenue, conversion, and customer experience. Partner with Account Management to support retention and expansion while improving scalable onboarding and support processes.

Build and evolve reusable AI capabilities for construction software products. Design production-grade LLM, retrieval, tool-use, orchestration, and agentic systems; establish evaluation-driven development and reliability standards; and prototype scalable AI solutions. Collaborate with product, engineering, and business stakeholders, guide other engineers, improve AI-assisted development practices, and support systems from experimentation through production rollout and continuous improvement.

Drive business development and close high-value enterprise deals with global biotech and biopharma accounts across Europe. Own strategic customer relationships, deliver recurring revenue, partner with technical and customer success teams on complex proposals, provide market insights for product direction, build industry credibility, and improve enterprise sales processes and playbooks.

Own the vision, roadmap, backlog, and delivery of AI-powered products. Translate business challenges into scalable solutions, define KPIs and business cases, prioritize initiatives, manage stakeholders, drive adoption, and measure business impact. Collaborate with AI engineers, data teams, IT, security, and governance stakeholders to develop reusable AI capabilities across markets and functions.

Conduct advanced research in artificial intelligence and machine learning, including deep learning, graphical models, reinforcement learning, computer perception, natural language processing, and data representation. Define complex business problems, design and deliver modeling solutions, evaluate model performance, establish scientific and engineering specifications, and build analytical roadmaps. Assess production implementation requirements, improve data capture methods, apply statistical and mathematical techniques, and resolve root causes and process bottlenecks.

Design, build, deploy, and evaluate production-grade generative AI applications using foundation models and LLMs. Develop RAG systems, AI agents, fine-tuned models, evaluation pipelines, and high-quality datasets for financial workflows. Optimize latency and cost while implementing scalable deployment, monitoring, safety guardrails, and responsible AI practices. Collaborate cross-functionally to deliver AI-powered solutions for finance and accounting teams.

Conduct research across the full machine learning lifecycle, including ideation, model design, implementation, evaluation, training, inference optimization, and deployment. Develop novel machine learning methods and foundation models, establish evaluation protocols, scale training with GPUs or TPUs, and improve research engineering infrastructure for rapid experimentation.

Lead delivery of real-time fraud decisioning services and platform capabilities for international markets. Design and implement backend services and APIs, integrate regional vendors and ML/AI signals, support operations and on-call, and mentor engineers to maintain high-quality, scalable systems.

Own the full sales cycle for AI compute services, including GPU infrastructure, managed inference, and serverless solutions. Build pipeline, qualify workloads, design solutions with architects, manage enterprise and public-sector sales, lead RFPs and pilots, close complex technical deals, and expand accounts. Develop partner relationships, forecast revenue, maintain CRM discipline, represent the company at events, and provide customer feedback to product and operations teams.

Develop and refine machine learning and LLM-based solutions, including agent architectures, embeddings, clustering, summarization, and automated workflows. Design experiments and metrics to evaluate model quality, build Java and Python services hosted on Snowflake and Kubernetes, troubleshoot systems, and collaborate with product, design, and partner teams to deliver features and shape product direction.

Design and develop scalable, high-quality backend systems for traffic automated road closure detection, handling the full software development lifecycle.

Leads NN Poland’s AI Enablement Team and Center of Excellence, shaping Data & AI strategy, governance, architecture standards, investment priorities, and an enterprise portfolio of AI initiatives. Evaluates opportunities, drives experimentation and proofs of concept, and enables secure, responsible, production-ready AI adoption. The role manages resources, budgets, capabilities, and cross-functional collaboration across business, technology, security, risk, legal, compliance, and data governance teams.