Top Machine Learning Jobs in Poland

Build and productionize ML models (segmentation, churn, LTV, recommendations), define ML problem scope and evaluation, collaborate with Product/Engineering/BI, monitor drift and retrain models, design production integrations and APIs, use telemetry and AI coding agents to accelerate development, and communicate results to technical and non-technical stakeholders.

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.

The Senior FinCrime Product Owner will design and implement a financial crime prevention product, develop strategy, and lead product delivery at Deblock.

Lead a Product Management team for Digital Connect API platform, define vision and roadmap, prioritize backlog, align with engineering and commercial teams, engage customers, support sales and delivery, monitor market and airline retailing trends, and promote practical AI use to improve product outcomes.

Own complex accounting provisions and reconciliations, design and maintain automated databases and models, apply automation and AI to finance processes, ensure month/quarter/year close accuracy, support audits, and partner with IT and global finance to standardize and improve accounting workflows.

Develop and implement 3D geometric ML and classical geometry algorithms to analyze and generate 3D intraoral scans and teeth. Build data pipelines, train/adapt 3D models, optimize test-time performance, validate robustness, and collaborate with clinicians and product teams to ship applied research into products.

Lead statistical strategy and delivery for Phase I-IV clinical trials embedded with a pharmaceutical sponsor. Design analyses, support protocol and reporting activities, oversee programming/CRO work, apply SAS/R (R Shiny)/Python and ML/DL methods for analyses and automation, and mentor junior statisticians.

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.

The Senior Machine Learning Engineer will execute software engineering tasks for machine learning operations, design MLOps systems, mentor team members, and ensure reliable ML systems using modern technologies.

Lead the organisation's AI and data strategy, prioritise high-impact use cases, and deliver dashboards, analytics, automation, and AI tools. Oversee data engineering, ML, governance, metadata, access controls, and responsible AI practices. Partner with business units to modernise workflows, build capability through training and playbooks, ensure compliance, and lead, coach, and develop multidisciplinary teams to improve productivity and decision-making.

Lead technical discovery and early development of an action-recognition CV product for retail. Own data collection/annotation, prototype and deploy end-to-end video ML pipelines, optimize models for precision, recall and latency, and provide technical leadership across product and engineering stakeholders.

Lead business analytics projects partnering with ABB business areas: design data migration, integration, validation, reporting and governance; manage cloud analytics platforms, vendors and infrastructure; pilot AI/ML solutions and ensure alignment with business objectives and controls.

Design and implement production-grade machine learning components for a next-generation bidder engine in AdTech. Collaborate with software engineers and architects to integrate ML into real-time bidding systems, replace rule-based logic with ML-driven decisioning, optimize performance, support deployment and monitoring, participate in architecture and design reviews, and mentor peers while driving operational excellence.

Build and maintain scalable data pipelines and warehousing to support ML and LLMs. Assist in model development, deployment, data transformation, quality, and performance optimisation, collaborate with data scientists, document workflows, and evaluate new AI technologies.

Join the MLOps team to deploy, maintain, monitor, and optimize machine learning models in production. Build data and model pipelines, implement CI/CD for ML, ensure model performance and cost efficiency, and collaborate cross-functionally to deliver scalable AI solutions.

Lead design, development, deployment, and maintenance of production-grade ML solutions across the full ML lifecycle for shipping and logistics. Build data pipelines, MLOps practices, model serving, monitoring, automated retraining, and mentor teammates while collaborating with business stakeholders.

Design, build, and productionize ML/NLP/LLM systems for clinical and financial analytics. Tune NLP engines, develop statistical and regression models, create private LLMs and RAG pipelines, deploy models on Databricks/Fabric/Azure ML, and build the enterprise AI analytics chatbot with monitoring and CI/CD.

Own and deliver ML-powered product features end-to-end: data pipelines, model integration, serving, monitoring, and iteration in production. Productionise research outputs, build ML infrastructure (model serving, inference pipelines, LLM integrations), improve reliability and cost-efficiency, collaborate with product engineers and scientists, and mentor junior engineers.

Provide technical leadership for ML-driven tooling and systems that improve Unreal Engine workflows, production pipelines, developer productivity and game subsystems. Collaborate with ML engineers and researchers to evaluate ML solutions, guide technical decisions, and translate capabilities into practical game development value. Manage and develop engineering and research staff, align cross-team efforts, and ensure delivery of high-impact tools and improvements.

Build and deploy production ML systems for large-scale ecommerce search and recommendations using text, image, and multimodal approaches. Tune and integrate LLMs and transformer models, develop end-to-end visual solutions, improve business KPIs through experimentation, and collaborate across teams to productize and scale ML capabilities.

Lead relevance evaluation and quality improvements by fine-tuning LLMs, building low-latency reranking and filtering systems, scaling LLM deployments, creating automated monitoring pipelines and agentic insights, and validating impact via A/B tests while collaborating with cross-functional teams.

Design, build, and improve ML models and production pipelines; collaborate with engineers, data scientists, and PMs; develop experimentation roadmaps; set up reproducible environments; monitor and maintain production models; document models and processes; propose and apply new AI/ML solutions.

Design, build, and maintain scalable AI platforms and data pipelines for precision oncology. Develop and deploy ML models (LLMs, VLMs, foundation models), implement MLOps, create APIs and services, and collaborate with clinicians and scientists to translate research into production-ready clinical and research tools.