Top Machine Learning Jobs in Poland

Lead enterprise AI strategy and advisory engagements, defining roadmaps, prioritizing use cases, building AI Centers of Excellence, and guiding initiatives into production. Advise C-level stakeholders, assess emerging technologies, develop investment cases, drive AI adoption and change management, facilitate strategic workshops, and support proposals and client relationships. The role requires strong AI expertise, strategic communication, stakeholder management, and technical judgment without daily hands-on implementation.

Own the vision, roadmap, and backlog for SEO and product content platforms within the Master Data domain. Drive scalable backend capabilities for metadata, content, faceted navigation, media management, structured data, crawling, indexing, and AI-supported discoverability. Collaborate with SEO, Content, Shop Management, Product, Data, and Engineering stakeholders, lead agile delivery, automate workflows, and improve discoverability, data quality, platform adoption, and operational efficiency.

Leads a team of MLOps engineers building and operating machine learning infrastructure, data pipelines, and model-lifecycle systems. Owns initiatives from design through production, mentors engineers, establishes technical standards, and partners with R&D to deliver scalable research and production workflows. The role involves hands-on coding, distributed computing, pipeline orchestration, cloud platforms, Kubernetes, Docker, and migration from Dask to Ray.

Owns core backend computation services that produce correct, reproducible, traceable, and auditable financial results. Designs configuration-driven architectures, data-intensive services, and set-based warehouse computations. Requires strong Python, SQL, data modeling, system design, reliability, isolation, and testing expertise. Provides technical leadership through design reviews, engineering standards, and mentorship while partnering with data, AI, and product teams.

Build and own scalable data architecture, canonical data models, configurable source integrations, and reliable pipelines for AI-driven financial intelligence. Use SQL, Python, dbt, cloud data warehouses, and orchestration tools to deliver idempotent, auditable data workflows. Establish lineage, data contracts, quality controls, and schema evolution practices. Lead architecture reviews, mentor distributed data engineers, and support customer-hosted multi-cloud deployments with strict tenant isolation and traceability.

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.

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, implement, optimize, and maintain complex large-scale software systems for airline network planning. Collaborate with globally distributed teams, solve architectural and algorithmic challenges, improve development processes and tooling, incorporate AI into workflows, mentor engineers, and drive software quality, stability, and operational excellence.

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.

Leads GxP validation, qualification, and testing across applications, data platforms, cloud infrastructure, and enterprise systems. Develops risk-based assurance strategies using CSA principles, authors and approves validation deliverables, oversees contractors and partners, supports AI-enabled application assurance, and improves validation methodologies, tools, automation, and audit readiness in collaboration with Digital, Quality, Compliance, and system owners.

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.

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 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.

Lead the transformation of legacy products into a unified enterprise platform. Translate strategy into roadmaps, manage backlogs, prioritize cross-functional initiatives, and coordinate Engineering, UX, AI, Data Science, Commercial, and Operations teams. Drive platform discovery, delivery, launch, adoption, and measurable business outcomes. Integrate trustworthy AI and data capabilities into customer-facing workflows while influencing senior stakeholders and balancing customer value, commercial goals, technical feasibility, risk, and resources.

Leads advanced data science initiatives for healthcare digital products, using predictive modeling, machine learning, statistical analysis, NLP, and large-scale data processing. Designs studies, validates models, engineers features, and develops scalable cloud-based analytics solutions. Collaborates with data engineers, developers, analysts, and business leaders to integrate models into products. Communicates insights through visualizations and reports, mentors data scientists, and establishes enterprise data science standards and practices.

Develop and maintain Java and Spring backend solutions for a high-traffic e-commerce platform. Build product relationship and variant-grouping mechanisms, process large-scale product data, and work with microservices, distributed systems, Big Data, cloud platforms, LLMs, machine learning models, and AI agents. Collaborate on complex technical and product challenges, with occasional frontend development opportunities.

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.

Design and implement data-driven applications and end-to-end data systems for clients. Responsibilities span cloud engineering, infrastructure, data engineering, ETL, machine learning, and data science. The role involves evaluating and presenting results, applying software engineering best practices, collaborating with data scientists and stakeholders, and delivering solutions through agile, iterative development.

Develop machine learning and generative AI solutions, build production web applications, analyze data, apply statistical methods, and collaborate with cross-functional teams using Agile practices. The role includes business requirements analysis, cloud application implementation, and delivering data-driven insights. Fresh graduates are welcome, with a contract ending in December 2028.

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.

Designs and leads cloud observability, monitoring, telemetry, and alerting strategy across Skywise platforms. Responsibilities include auditing existing tools, defining a unified operations roadmap, optimizing cloud costs, integrating AIOps for anomaly detection and automated response, harmonizing monitoring tools, promoting SRE practices, and transitioning platform ownership from external consultants. The role requires AWS expertise, experience with enterprise observability platforms, infrastructure as code, platform operations, and cross-functional leadership.