Top Machine Learning Jobs in Warsaw

Lead and develop globally distributed Data and AI teams to deliver production Data/AI solutions. Drive talent development, operational excellence, stakeholder alignment, and responsible AI practices while partnering with Product and Engineering to set priorities, roadmaps, and delivery metrics.

Lead organization-wide software and AI engineering strategy, design scalable cloud-native systems, drive deployment and data integration best practices, and deliver rapid prototype-to-production solutions. Own LLM and ML strategy including model fine-tuning, RAG, AI evaluation, and observability. Mentor technical leaders, shape developer experience and knowledge management, and represent engineering at executive and industry levels.

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

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.

Partner with Market Units to analyze product, customer, and market data, produce pricing dashboards and reports, run workshops, track and improve pricing initiatives, and develop PriceFX analytics and AI-driven pricing agents while training users and collaborating with IT and data science teams.

The role involves leading a team of Data Science Engineers to develop sports analytics and predictive models, ensuring high quality of data science workflows and collaboration with the Engineering Manager.

The Data/Information Architect designs and maintains the information landscape, streamlining data flows and establishing governance standards, while collaborating with stakeholders to optimize data processes.

Support development and fine-tuning of ML and Agentic AI solutions for pharma/healthcare. Assist in building, testing, and validating prototypes and production-grade algorithms using large real-world datasets. Collaborate with ML engineers, researchers, and clients to deliver deployable AI products.

Lead organization-wide adoption of AI and Data Science within Nuclear Engineering to drive Engineer 2030 productivity and $800M EBITDA growth. Build and manage a high-performing team, define AI strategy, ensure regulatory governance and compliance, apply Lean+AI to eliminate waste, and deliver measurable financial and productivity outcomes.

Develop, implement, and document expected loss forecasting models (PD/LGD/EAD) for commercial portfolios to meet CECL/CCAR and regulatory requirements. Analyze model performance, refine segmentation, support stress-testing submissions, and respond to regulators. Use Python/SAS/R/SQL and cloud tools, leverage automation and AI to improve efficiency, and provide model governance and validation-ready documentation.

As an Applied AI Engineer, you will architect, build, and deploy enterprise-grade AI solutions, work with customers to address complex challenges, and collaborate with cross-functional teams to innovate using AI tools.

Design and deploy production-grade systems integrating machine learning models into scalable software pipelines, while focusing on maintainability, performance, and collaboration with teams.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.

The Technical Project Manager will drive the delivery of technical features across platforms, manage project lifecycles, and facilitate communication in cross-functional teams.

Work with traders, asset operators, and developers to analyze electricity market outcomes, improve trading and operational strategies, and drive customer success. Translate market workflows into product requirements, collaborate with engineering to enhance analytics, dashboards, and optimization models, and lead customer onboarding, implementation, and ongoing technical account management.

Lead product strategy and roadmap for cloud-native retail platforms (POS, AI/Computer Vision, loyalty, promotions). Manage ~20 product professionals, drive migrations, internationalization, compliance, and cross-functional alignment to grow ARR, adoption, and platform scalability.

Lead the product vision, roadmap, and backlog for AI-powered HR and workforce management SaaS solutions. Collaborate with stakeholders, engineering, AI specialists and UX to define, prioritize, and deliver generative AI, conversational AI, and automation features; validate hypotheses, define success metrics, ensure secure and auditable AI integrations via APIs, and act as the customer advocate balancing business value and technical feasibility.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

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.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Lead end-to-end process reengineering and automation initiatives to standardize client engagement, drive operational excellence, implement RPA/AI solutions, manage integrations, enforce governance, define KPIs, and foster continuous improvement across global stakeholders.

Lead Salesforce product initiatives for Channel and Enterprise sales: gather and document requirements, build roadmaps, define solutions, drive implementation, enable adoption, produce reports, and partner with sales, sales ops, IT and enablement to improve selling effectiveness and reduce CRM admin burden.

You will design and optimize LLM-based machine learning solutions, focusing on natural language processing, deep learning methods, and inferencing pipelines.