Top Machine Learning Jobs in Poland

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.

Optimize the performance of renewable energy assets, particularly battery energy storage systems, through performance analysis, machine learning, forecasting, condition monitoring, and fault prediction. Identify upgrade opportunities, assess technical and commercial viability, investigate lost production and root causes, and advise asset supervisors on technical and performance issues.

Develop and maintain ROS-based robotics solutions for visitor experiences, operations, and educational programs. Responsibilities include perception and navigation software, firmware, hardware calibration, testing, monitoring, deployment maintenance, and collaboration with external partners. The role involves hands-on work with LiDARs, IMUs, Linux environments, and robotics hardware, with opportunities to progress into core software development, AI, and full-stack robotics work.

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.

Lead Business Analysis activities for Consumer Intelligence: shape and improve BA practices, drive demand intake and prioritization, maintain roadmap, apply agile and UAT best practices, coordinate cross-team delivery, and ensure alignment with business objectives and internal controls.

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.

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.

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.

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.

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.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

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.