Top Machine Learning Jobs in Poland

Design, develop, and optimize high-performance Go backend services for a real-time advertising bidder. Analyze distributed systems, resolve scalability and latency issues, integrate data-driven and machine learning capabilities, collaborate with product and engineering teams, and oversee features from concept through production.

Designs enterprise-grade cloud, data, and AI solutions for airline operations. Responsibilities include assessing technology landscapes, identifying modernization and integration opportunities, translating business priorities into technical architectures, creating solution blueprints and documentation, promoting enterprise architecture best practices, and guiding customer teams through transformation initiatives.

Design and lead multi-domain cloud, data, and AI architectures for aviation operations. Translate business needs into scalable technical solutions, guide technology decisions, oversee platform integration, and provide technical leadership and mentorship to customer development teams. Collaborate with technical and business stakeholders while balancing performance, security, scalability, and cost.

Design, train, evaluate, and optimize machine learning models for camera-based automotive perception. Apply transformers, vision transformers, CNNs, representation learning, and self-supervised learning to improve robustness, generalization, efficiency, and inference latency. Collaborate with software, systems, validation, and AI/ML teams while supporting software quality, documentation, testing, and engineering excellence. Work focuses on computer vision features such as vehicle, pedestrian, lane, traffic sign, and traffic light detection.

Develop complex cloud applications for the Altium 365 platform. Build cloud-native applications and scalable infrastructure, design APIs and data models, implement stateful and stateless services, work with databases and ORM frameworks, and contribute to microservices and cloud solutions. The role requires technical ownership, collaboration, problem-solving, and influence over engineering standards, best practices, and technical governance.

Develop complex cloud applications for the Altium 365 electronics design platform. Build cloud-native applications, scalable infrastructure, microservices, APIs, data models, servers, databases, and ORM-based systems. The role requires solving complex technical challenges, influencing technical direction, defining engineering standards and governance, and working autonomously while collaborating with a global engineering team.

Lead the AI/ML core for a robotics automation startup: design, train, and fine-tune models, build computer vision pipelines, integrate AI with hardware, define the AI stack, mentor engineers, and translate customer feedback into technical requirements.

Lead the strategy, design, deployment, and scale-up of AI-powered health products across the Global South. Own product roadmaps, execution, government-system integration, stakeholder adoption, partnerships, grant development, and portfolio expansion. Collaborate with engineering, machine learning, program, and domain teams to deliver sustainable, measurable public-health impact.

Architect and build the AI layer for Neo4j’s Hume Maestro platform. Responsibilities include setting the AI technical roadmap, contributing hands-on to production code, designing scalable AI architectures, establishing model evaluation and deployment practices, integrating AI with front-end and platform systems, and collaborating with Product and Science stakeholders. The role may eventually evolve into leading and mentoring a dedicated AI engineering team.

Own full-cycle recruiting for technical roles, partner with hiring managers to define roles, source and close candidates, build scalable systems and pipelines, optimize hiring with data, and support a global, remote people team while acting as a culture ambassador.

Develop and validate machine learning and optimization models for demand, supply and inventory planning. Translate business problems into scalable solutions, support deployment and monitoring, collaborate with analysts and engineers, and embed analytical outputs into planning processes to drive measurable business value.

Lead a cross-functional team building ML-powered marketplace catalogue systems, including product enrichment, categorization, translation, moderation, embeddings, search, and recommendations. Own the product data pipeline, ML roadmap, evaluation and labeling infrastructure, reliability, technical debt, hiring, team development, OKRs, and cross-functional delivery. Ensure models and services meet quality, cost, latency, scalability, observability, and data residency requirements.

Lead the design and delivery of production conversational AI systems, including multi-turn agents, LLM reasoning loops, and multi-agent orchestration. Use Python, Azure, MLflow, and Databricks to build and maintain end-to-end AI solutions. Mentor scientists, lead cross-functional initiatives, promote AI best practices, collaborate with customers, and guide adoption of conversational AI across enterprise sustainability use cases.

Own backend and API-facing products for Tatari’s programmatic TV advertising platform, including auction optimization, exclusion logic, scoring models, and publisher integrations. Write PRDs, user stories, and acceptance criteria; manage Agile product activities; monitor campaign performance; investigate anomalies; and partner with engineers, data scientists, AdOps, QA, publishers, and clients. Requires technical ad tech expertise, VAST or RTB integration experience, strong data reasoning, fluent Ukrainian and English, and residence in Ukraine or Europe.

The Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

Senior technical leader providing high-level AI strategy, architecture direction, and long-term systems design. Delivers strategic artifacts (architecture reviews, AI integration strategies, roadmaps), advises executives, collaborates with product and engineering teams, and mentors technical leaders to improve scalability, resilience, and governance across platforms.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.

Design and execute benchmarks, curate datasets, evaluate benchmarks, and build evaluation infrastructure for AI model assessment while collaborating with R&D.

Design, implement, and optimize performance-critical GPU-style kernels (e.g., matmul, attention) and host-side orchestration. Identify bottlenecks, deliver measurable throughput improvements, create micro-benchmarks, regression tests, and tooling, and collaborate with compiler, runtime, ML, and hardware teams to integrate kernels into production.

Lead Data, Analytics, AI, and GenAI presales activities for strategic enterprise opportunities. Facilitate discovery workshops, translate business needs into scalable architectures, lead RFP and RFI responses, and present solutions to executives. Design end-to-end AI and cloud architectures across Azure, GCP, Databricks, and open-source ecosystems. Advise on AI transformation use cases, roadmaps, governance, and implementation, while mentoring delivery teams and developing reusable frameworks, offerings, thought leadership, and customer success materials.

Build Solidgate’s new ML/AI function, developing production machine learning models, recommendation systems, and real-time transaction-routing decision systems. Own experimentation from ideation through A/B testing and deployment, engineer features from high-cardinality transactional data, integrate emerging AI technologies, and collaborate with Product, Engineering, and Data Platform teams on training and analytics pipelines.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.

Research and investigate global fraud trends using BioCatch data and external sources. Produce data-driven insights, visualizations, written thought leadership, dashboards, and presentations. Build repositories and automate analysis workflows while applying fraud expertise and AI tools to validate findings and support sales and marketing.

The Data Scientist will design advanced algorithms for generative AI models, analyze large datasets, and collaborate on AI advancements to enhance business processes.

Develops and scales predictive analytics and machine learning models for commercial decision-making. Responsibilities include forecasting, pricing optimization, profitability and tender analytics, customer behavior modeling, opportunity identification, model lifecycle management, and embedding analytical solutions into business processes. Partners with Commercial, Marketing, Finance, and IT stakeholders to translate business needs into scalable, interpretable insights and measurable growth outcomes.