Top Machine Learning Jobs in Poland

Develop and improve quantitative supply-and-demand models and forecasts for power markets, integrate weather and climate data, apply machine learning and AI methods, process large time-series and geospatial datasets, produce analysis and reports for market participants, and collaborate across research, data science, engineering and product teams.

Designs and deploys production AI/ML solutions and agentic workflows, translating ambiguous business problems into modeling approaches. Builds GenAI evaluation frameworks, golden datasets, testing harnesses, and MLOps/LLMOps pipelines with observability. Implements governance, security, fairness, privacy, and explainability controls while partnering with product, engineering, and business stakeholders on AI adoption, impact, and cost optimization.

Develop and maintain high-load, real-time personalization services for global e-commerce platforms. Improve shopper experiences and business KPIs through applied machine learning, user interaction modeling, personalization and recommender systems, hypothesis validation, and A/B testing. Collaborate with engineering teams and business stakeholders to research, deliver, and communicate personalization capabilities and outcomes.

Design, deploy, and optimize machine learning models and infrastructure using Databricks. Lead end-to-end model development, including data preparation, deployment, monitoring, and production scaling. Collaborate with data scientists, engineers, business stakeholders, and clients to deliver technical solutions. Mentor junior team members and apply advances in machine learning, NLP, computer vision, and MLOps to solve complex business problems.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Own enterprise customer relationships from kickoff through adoption and ongoing value realization. Lead success plans, QBRs, executive conversations, escalations, stakeholder alignment, training, and strategic recommendations. Translate technical capabilities and performance data into business outcomes such as revenue, conversion, and customer experience. Partner with Account Management to support retention and expansion while improving scalable onboarding and support processes.

Build and evolve reusable AI capabilities for construction software products. Design production-grade LLM, retrieval, tool-use, orchestration, and agentic systems; establish evaluation-driven development and reliability standards; and prototype scalable AI solutions. Collaborate with product, engineering, and business stakeholders, guide other engineers, improve AI-assisted development practices, and support systems from experimentation through production rollout and continuous improvement.

Drive business development and close high-value enterprise deals with global biotech and biopharma accounts across Europe. Own strategic customer relationships, deliver recurring revenue, partner with technical and customer success teams on complex proposals, provide market insights for product direction, build industry credibility, and improve enterprise sales processes and playbooks.

Own the vision, roadmap, backlog, and delivery of AI-powered products. Translate business challenges into scalable solutions, define KPIs and business cases, prioritize initiatives, manage stakeholders, drive adoption, and measure business impact. Collaborate with AI engineers, data teams, IT, security, and governance stakeholders to develop reusable AI capabilities across markets and functions.

Conduct advanced research in artificial intelligence and machine learning, including deep learning, graphical models, reinforcement learning, computer perception, natural language processing, and data representation. Define complex business problems, design and deliver modeling solutions, evaluate model performance, establish scientific and engineering specifications, and build analytical roadmaps. Assess production implementation requirements, improve data capture methods, apply statistical and mathematical techniques, and resolve root causes and process bottlenecks.

Conduct research across the full machine learning lifecycle, including ideation, model design, implementation, evaluation, training, inference optimization, and deployment. Develop novel machine learning methods and foundation models, establish evaluation protocols, scale training with GPUs or TPUs, and improve research engineering infrastructure for rapid experimentation.

Lead delivery of real-time fraud decisioning services and platform capabilities for international markets. Design and implement backend services and APIs, integrate regional vendors and ML/AI signals, support operations and on-call, and mentor engineers to maintain high-quality, scalable systems.

Own the full sales cycle for AI compute services, including GPU infrastructure, managed inference, and serverless solutions. Build pipeline, qualify workloads, design solutions with architects, manage enterprise and public-sector sales, lead RFPs and pilots, close complex technical deals, and expand accounts. Develop partner relationships, forecast revenue, maintain CRM discipline, represent the company at events, and provide customer feedback to product and operations teams.

Develop and refine machine learning and LLM-based solutions, including agent architectures, embeddings, clustering, summarization, and automated workflows. Design experiments and metrics to evaluate model quality, build Java and Python services hosted on Snowflake and Kubernetes, troubleshoot systems, and collaborate with product, design, and partner teams to deliver features and shape product direction.

Design and develop scalable, high-quality backend systems for traffic automated road closure detection, handling the full software development lifecycle.

Leads NN Poland’s AI Enablement Team and Center of Excellence, shaping Data & AI strategy, governance, architecture standards, investment priorities, and an enterprise portfolio of AI initiatives. Evaluates opportunities, drives experimentation and proofs of concept, and enables secure, responsible, production-ready AI adoption. The role manages resources, budgets, capabilities, and cross-functional collaboration across business, technology, security, risk, legal, compliance, and data governance teams.

Develops in-vehicle data quality checks, validation-drive automation, data insights, ETL solutions, APIs, data pipelines, and processing tools for autonomous driving systems. Collaborates with engineering, product, data science, and domain teams while adapting big-data technologies for cloud platforms. Requires Python, C++ or Rust, SQL, scripting, machine learning, data preparation, statistics, and debugging expertise. Experience with cloud, containers, embedded AI, automotive datasets, Git, CI/CD, and ISO 26262 is beneficial.

Build and productionize Stable Diffusion, computer vision, and machine learning pipelines for photorealistic talking virtual humans. Responsibilities include dataset development, model training, research prototyping, ML service architecture, deployment, monitoring, optimization, and cloud or on-device inference. The role also evaluates emerging techniques and may involve DSP, NLP, C++, and CUDA.

Provides strategic and hands-on biostatistical leadership across pharmaceutical discovery, translational, biomarker, and early research programs. Designs studies, analyzes complex biological datasets, develops advanced statistical methods and reproducible R workflows, and translates evidence into portfolio decisions. The role collaborates with quantitative and scientific functions, supports publications and external partnerships, mentors statisticians, and advances data-driven analytical practices.

Develops LLM-powered features and RAG pipelines for intelligent search and knowledge graph platforms. Responsibilities include data ingestion, preprocessing, vector and hybrid retrieval, model evaluation, relevance tuning, production deployment, testing, and end-to-end ownership of AI components.

Provide technical support for Workday’s enterprise platform and AI agent workflows. Responsibilities include incident resolution, root-cause analysis, cloud and ML pipeline troubleshooting, SQL and log analysis, AI conversation and LLM output evaluation, queue triage, SLA management, customer escalations, and collaboration with engineering and data science teams. The role includes weekend on-call rotations and requires translating technical issues into clear customer communications.

Design and deploy end-to-end document intelligence pipelines on AWS. Develop machine learning models for document classification, segmentation, and field extraction; build scalable PDF processing systems; and apply generative AI, RAG, prompt engineering, and multimodal models. Own features from research through production, establish evaluation frameworks and quality metrics, collaborate with subject matter experts, and communicate technical solutions effectively.

Build and maintain production-grade native iOS applications using Swift and SwiftUI, integrating AI features through backend APIs. Design resilient real-time user experiences, optimize performance and resource usage, collect telemetry, and collaborate with ML and backend teams. The role requires shipping consumer-facing App Store applications with reliable networking, graceful degradation, concurrency, and on-device machine learning capabilities.

Leads complex, cross-functional technical programs for studio infrastructure, cloud environments, hardware provisioning, data migrations, software integrations, and Generative AI/ML platform initiatives. Develops technical strategies, manages dependencies, risks, scope, schedules, and Day 1 readiness, while communicating progress and business impact to stakeholders and leadership across globally distributed teams.

Own an AI investing product vertical end-to-end, including strategy, metrics, roadmap, technical design, delivery, and launch. Partner with engineering on APIs, data flows, ledgering, reliability, security, and scalability. Incorporate KYC, transaction monitoring, limits, privacy, licensing, and regulatory requirements. Apply AI with evaluation, safeguards, human oversight, and failure handling while coordinating Engineering, Design, Compliance, Operations, Treasury, and Finance.