Top Machine Learning Jobs in Poland

Builds data engineering solutions for in-vehicle data quality, validation-drive automation, data triaging, and insights supporting hands-off urban driving. Responsibilities include developing APIs, cleaning and organizing raw and processed data, implementing analytics and machine learning solutions, and collaborating with engineering, product, and domain teams. The role involves cloud-based ETL and big-data systems connected to embedded driver-assistance technologies.

Partners with business leaders and cross-functional teams to deliver advanced analytics, sales force effectiveness insights, standardized reporting, and strategic recommendations. Uses SQL, statistical tools, visualization platforms, AI, and machine learning to analyze healthcare and commercial data, track performance, guide investments, and improve customer experience. The role also supports targeting, deployment, incentives, CRM, forecasting, segmentation, and data-driven decision-making across the Poland cluster.

Develop machine learning and analytics solutions using large-scale datasets. Responsibilities include feature engineering, predictive modeling, workflow optimization, customer implementations, production deployments, stakeholder collaboration, technical training, and resolving data-related challenges. The role requires Python, PySpark, machine learning pipelines, MLflow or Airflow, statistical modeling, and strong communication skills, with preferred experience in fraud detection, risk analytics, compliance, cloud environments, and model monitoring.

Processes investor transaction documents, maintains capital activity reports, completes AML requirements, prepares money movement notifications and wire instructions, and serves as a daily client contact. The role supports investor servicing operations, meets service-level deadlines, develops knowledge of allocations and performance calculations, mentors junior staff under supervision, and contributes to projects and ad hoc tasks.

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.

Designs, develops, and scales autonomous AI agents and workflow orchestration solutions using LLM frameworks such as n8n. Builds rapid proofs of concept, integrates agents with enterprise CRM, ERP, and SaaS systems, establishes responsible AI governance, evaluates automation effectiveness, and trains internal teams on agentic AI adoption.

Develops data-processing solutions, quantitative models, machine-learning applications, and automated database pipelines for legal entity content. Analyzes patterns and trends, builds proofs of concept, visualizations, and analytics tools, and supports deployment with development teams. The role collaborates with business, content, product, and data strategy stakeholders while communicating findings to technical and non-technical audiences.

Develops and deploys traditional machine learning, deep learning, reinforcement learning, generative AI, and agentic AI models for sales analytics and pricing optimization. Responsibilities include data preprocessing, feature engineering, model validation and monitoring, stakeholder collaboration, technical documentation, AI governance, and mentoring junior team members. The role uses Python, SQL, Google Cloud Platform, and related data and AI technologies in an international banking environment.

Develop and validate machine learning models for document classification and information extraction within Commerzbank’s DocAI+ initiative. Analyze data and pipelines, advise stakeholders, improve AI-supported processes, evaluate advanced solutions, and ensure compliance with AI governance. Collaborate in an international agile team using approved cloud platforms and technologies.

Develop and deploy data science, machine learning, generative AI, and agentic AI solutions for sales analytics and optimized pricing. Responsibilities include data preprocessing, feature engineering, model validation, monitoring, optimization, stakeholder collaboration, technical documentation, AI governance, and mentoring junior colleagues. The role uses Python, SQL, Google Cloud Platform, and related AI frameworks in an international, hybrid banking environment.

Build, deploy, monitor, and improve production machine learning systems for customer and revenue use cases. Develop scalable data and ML pipelines, work with structured and unstructured data, apply modern ML and LLM techniques, and ensure model reliability through testing and validation. Collaborate with data engineers, product managers, and business stakeholders to deliver end-to-end solutions that improve business outcomes.

Lead advanced data science projects, develop AI/ML products, build ML-powered web applications, and manage product lifecycle while collaborating across functions.

Leads the design, development, deployment, and evaluation of machine learning, forecasting, recommendation, and analytical solutions. Partners with Product, Commercial, Engineering, and Data teams to solve business problems, define metrics, build scalable pipelines, and deliver measurable impact. Provides technical leadership and mentorship, promotes model development and governance best practices, and contributes to the data science roadmap.

Leads a supervisory team responsible for AI risk management in the financial sector. Develops supervisory frameworks, addresses AI policy and regulatory issues, coordinates with government agencies and international standards bodies, supports organizational AI strategy, and directs research on emerging AI risks and mitigation. Manages team resources, project timelines, and deliverables while engaging technical and non-technical stakeholders.

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.

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.

Owns full-cycle recruiting for commercial, operational, and technical roles across global markets. Responsibilities include sourcing passive candidates, building pipelines for hard-to-fill positions, advising hiring leaders, coordinating interviews and offers, maintaining accurate Greenhouse data, and improving hiring processes through systems thinking, data, AI, and automation.

Analyzes large datasets to identify platform performance and reliability issues, develops forecasting and early-warning systems, and builds software tools that automate analytical workflows. The role applies statistical analysis, machine learning, networking knowledge, and coding to diagnose content delivery challenges. It partners with Engineering, Operations, and Product teams to guide technology adoption and investment decisions. This is explicitly not a QA, DevOps, or Operations role.

Build BLP’s core software across infrastructure, backend, machine learning, and product extension platforms. Responsibilities include operating secure and observable cloud infrastructure, developing performant distributed systems, applying AI to business problems, and enabling customer process automation. The role emphasizes formal reasoning, correctness by construction, maintainable systems, type systems, and functional programming. Work is available onsite in Zurich or remotely within Europe.

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.

Develop and improve computer vision systems focused on aerial image recognition for a roofing technology platform. Build, train, evaluate, fine-tune, and deploy models for detection, segmentation, classification, and pattern recognition. Research and prototype modern vision, foundation, multimodal, and language-model solutions; optimize accuracy, reliability, and inference performance; prepare data pipelines; and identify product opportunities for AI. The role requires independent ownership of open-ended technical challenges from research through deployment.

Develop and deploy computer vision and machine learning systems for aerial image recognition in construction technology. Responsibilities include object detection, segmentation, classification, model training and optimization, rapid prototyping, multimodal AI experimentation, data pipeline development, and researching AI applications that improve product functionality and user experience.

Develop and improve computer vision systems focused on aerial image recognition. Build, train, fine-tune, evaluate, and deploy models for detection, segmentation, classification, and pattern recognition. Research modern vision, foundation, multimodal, and language models; rapidly prototype ideas; optimize accuracy and inference performance; create data pipelines; and identify AI opportunities across the product. The role requires ownership of technical challenges from research through implementation.

Own the strategy, roadmap, execution, and business outcomes for MEDvidi’s AI Receptionist product. Lead cross-functional collaboration across Product, Engineering, Clinical, and Operations teams; improve conversational AI quality through experimentation, evaluation, analytics, and user feedback; define success metrics; and ensure safety, privacy, and reliability in regulated healthcare workflows.

Lead the technical vision, design, development, and deployment of AI-powered mobile and edge solutions across Android, iOS, and Windows. Build production-ready machine learning pipelines, AI services, device integrations, observability capabilities, and scalable mobile applications. Mentor engineers and collaborate with product, UX, architecture, and business teams. The role emphasizes IoT, embedded systems, edge AI, generative AI, computer vision, predictive analytics, and regulated-industry applications.