Top Machine Learning Jobs in Warsaw

Leads clinical data science strategy, delivery, and people development across pharmaceutical clinical development programs. Oversees quantitative analyses using clinical, biomarker, real-world, and high-dimensional data; guides statistical, machine learning, simulation, and visualization methods; and ensures analysis-ready, traceable, reproducible, submission-ready outputs. Partners with clinical, regulatory, data management, pharmacovigilance, programming, technology, and external teams. Supports health authority interactions, regulatory submissions, inspection readiness, resource planning, risk mitigation, standards, automation, and capability building.

Lead clinical data science and statistical analyses for pharmaceutical development programs. Develop analysis-ready datasets, reproducible workflows, visualizations, simulations, and quantitative insights using clinical trial, biomarker, real-world, and external data. Support integrated data review, data quality assessment, regulatory compliance, and evidence-based study decisions. Collaborate with cross-functional teams, review internal and external deliverables, advance automation and AI/ML adoption, and mentor less experienced colleagues.

Manages clinical data science activities supporting pharmaceutical development studies. Performs exploratory analyses, visualization, quantitative assessments, data review, and interpretation across clinical, biomarker, real-world, and other data sources. Develops analysis-ready datasets, reproducible workflows, specifications, and submission-ready deliverables using statistical, machine learning, automation, and advanced analytics methods. Collaborates with cross-functional study teams, communicates data and analytical risks, and supports regulatory-compliant, traceable, high-quality decision-making.

Leads a clinical data science team supporting pharmaceutical development, regulatory submissions, and inspection readiness. Oversees statistical and machine learning analyses using clinical trial, biomarker, real-world, and external data. Sets analytical strategy, ensures traceable and submission-ready datasets and outputs, manages vendors and resources, partners cross-functionally with global regulatory teams, and develops direct reports and broader team capabilities.

Lead clinical data science and statistical analytics across pharmaceutical development studies. Design analyses using clinical, biomarker, real-world, and external data; oversee partners; define regulated, submission-ready data flows; apply statistics, machine learning, simulation, and visualization; manage analytical risks; mentor colleagues; and advance reproducible, automated, AI-enabled clinical data science practices.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Lead software engineering efforts for Samsara’s External Platform EMEA, building scalable distributed systems, APIs, reporting, administration, integrations, and business-critical infrastructure. Lead technical discussions, influence architecture and roadmap, deliver full-stack features, maintain production systems, and support customer-focused iteration. Mentor engineers, hire and develop high-performing teams, and contribute to machine learning-powered products and long-running projects.

Develop predictive models and quantitative analytics for vulnerability prioritization within an enterprise security program. Analyze large-scale security telemetry, build machine learning and statistical models to reduce scanning noise and false positives, and create unified dashboards supporting data-driven security governance. The role requires autonomous commercial data science experience applied to vulnerability management, along with advanced Python, R, SQL, TensorFlow, and PyTorch skills.

Lead the data and AI function for a new fintech platform, owning strategy, execution, budget, and organization building. Establish data engineering, analytics, ML, MLOps, and data product teams; develop a governed Databricks lakehouse, real-time decisioning, feature store, agentic systems, and measurable data products. Ensure models and platform capabilities are production-ready, explainable, auditable, and compliant while reporting progress and risks to executive and board stakeholders.

Lead the creation of the ML and MLOps function, team, platform, and development standards from the ground up. Own the full machine-learning lifecycle, including credit and decisioning models, validation, deployment, monitoring, retraining, model-risk management, responsible AI, and regulatory governance. Manage compute budgets, headcount, and measurable business outcomes while partnering with data engineering on governed feature production.

Build Solidgate’s ML/AI function from the ground up, developing real-time payment routing models and production pipelines. Own the full ML lifecycle from experimentation and A/B testing through deployment, while solving modeling challenges involving large-scale transactional data. Partner with Product, Engineering, and Data Platform teams to deliver measurable improvements in conversion, approval rates, losses, and revenue.

Lead regulatory engineering projects for Global Banking & Markets, designing, developing, testing, and migrating secure, scalable distributed systems to cloud-native platforms. Responsibilities include technical leadership, event-processing architecture, stakeholder management, compliance with engineering practices, performance monitoring, and implementation of AI-enabled workflows. The role requires expertise in real-time systems, cloud architecture, containerization, NoSQL technologies, production software development, and emerging AI techniques including generative interfaces, RAG, fine-tuning, and multi-agent systems.

Investigate customers and potential matches against Sanctions and KYC/AML lists during account opening and maintenance. Conduct searches across internal systems, the Internet, and commercial databases; gather and document evidence; assess customer risk; and collaborate with Business and Compliance partners. Analysts apply training, meet service timeframes, and build expertise in sanctions screening, KYC processes, and AI-enabled financial crime detection.

Develop and operate complex, cloud-native platform applications for Altium 365. Build scalable infrastructure, distributed services, APIs, data systems, and advanced search capabilities. Design and optimize high-load systems, influence architecture across teams, and establish engineering standards and technical governance. The role uses .NET, AWS, microservices, databases, containers, Kubernetes, and CI/CD tooling across more than 100 services.

Build and lead production LLM systems, including agentic workflows, content enrichment, retrieval, semantic search, evaluation, observability, and safety guardrails. Own architecture decisions across model selection, prompting, fine-tuning, latency, cost, and quality. Partner with Product and UX to ship AI features, mentor engineers, lead technical reviews, and establish reusable LLM development practices.

Design, automate, and maintain credit risk data, ETL and reporting processes. Build Power BI dashboards, automate workflows with Microsoft Power Platform, write SQL/Python queries, partner with risk managers, and apply ML/AI to improve reporting and decision-making.

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.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

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.

Leads Engineering Excellence and Quality Engineering practices across a global software organization. Responsibilities include developing quality engineering teams, establishing engineering standards and governance, advancing test automation and AI-assisted testing, embedding quality and security into CI/CD, and improving performance, reliability, observability, and release readiness. The role uses engineering metrics and quality data to drive continuous improvement, partners with engineering and product stakeholders, and supports software validation and regulatory compliance in highly regulated environments.

Lead and grow the Money & Risk engineering team responsible for fraud prevention, risk scoring, reconciliation, settlement, and chargebacks. Own technical direction for scalable, auditable, secure payment systems across 80+ countries. Drive architecture, engineering standards, AI-assisted development, hiring, coaching, and cross-functional delivery with Product, Data, Finance, Compliance, and Operations.

Build and scale machine learning systems for entity resolution across hundreds of millions to billions of records. Responsibilities include developing matching, clustering, deduplication, similarity, and search algorithms; engineering Spark and SQL pipelines; managing Airflow and dbt workflows; modeling data in Snowflake; and improving precision, recall, accuracy, reliability, and cost efficiency. The role requires hands-on Python, distributed systems, production ML, large-scale data engineering, and algorithmic problem-solving expertise.

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.

Own the Mapping & Content product domain end to end, including strategy, prioritization, economics, and outcomes. Lead efforts to match hotel rooms and content across suppliers, languages, and formats with high accuracy and coverage. Partner closely with engineering and data science teams on entity resolution, search, matching, catalog, identity, recommendation, and content-quality problems. Identify high-value failures, make trade-offs between precision and coverage, and help build Nuitee’s product organization in a fast-moving environment.

Lead research on interactive avatar models and real-time generative AI video agents. Set research direction, advance multimodal perception and reaction generation, post-train diffusion models, build evaluation frameworks, define datasets, and guide research-to-production efforts. Mentor researchers and influence technical decisions across research, data, and engineering teams.