Top Machine Learning Jobs in Poland

Develop and operate complex, cloud-native applications for the Altium 365 platform. Responsibilities include building scalable infrastructure, distributed high-load systems, APIs, data models, microservices, and platform frameworks. The role involves performance optimization, testing, architectural decision-making, technical governance, and influencing engineering standards across multiple teams. Engineers may also contribute to AI-powered search, identity and authentication, access management, messaging, caching, databases, and multi-tenancy capabilities.

Own the global B2B content strategy for enterprise audiences, creating website copy, advertising, whitepapers, case studies, thought leadership, social content, video scripts, and sales enablement materials. Build AI-assisted content workflows with prompt libraries, source-of-truth repositories, context engineering, quality controls, and human review. Partner with Product, Sales, and regional Marketing teams to communicate complex AI products and optimize content using performance data.

Develop computer vision and machine learning models that analyze biological and microscopy images to identify medical features and biomarkers. The role requires Python, image processing, and PyTorch or TensorFlow experience, along with independent problem-solving, strong communication, and collaboration across distributed international teams. The engineer will work remotely on an AI-driven MedTech diagnostic project with significant technical ownership.

Designs and specifies 5G radio network system features, algorithms, test cases, and use cases, primarily focused on Radio Resource Management. Develops network-level simulations, analyzes performance data, supports system verification and customer troubleshooting, contributes to 5G/6G standardization and patents, and assists with deploying products into live networks. Collaborates with global development, customer, and cross-functional teams.

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.

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.

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.

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.

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.

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.

Explore, evaluate, and operationalize emerging AI methodologies, models, architectures, and technologies. Design experiments, benchmarks, evaluations, and proofs of concept; develop and productize generative, agentic, multimodal, and machine learning solutions; translate research into reusable enterprise capabilities; and collaborate with platform, data, product, architecture, and engineering teams to scale AI across Danaher.

Lead a distributed engineering squad building and operating email security products. Own delivery planning, architecture, code and design reviews, production reliability, SLOs, incident response, model quality gates, technical debt, and roadmap development. Coach, hire, and develop engineers while establishing effective remote-work practices. The role combines approximately 60% technical leadership and people management with 40% hands-on engineering, including debugging, production changes, and AI-assisted development practices.

Leads Danaher’s global AI Engineering, Integration and Operations organization, converting AI prototypes and capabilities into secure, scalable, production-grade solutions. Responsibilities include establishing engineering and operational practices, leading multidisciplinary teams, partnering with product and governance leaders, planning engineering capacity, and ensuring reliable deployment, support, adoption, and continuous improvement of enterprise AI solutions.