Top Machine Learning Jobs in Poland

Own production optimization of LLM and VLM inference systems, improving latency, throughput, memory efficiency, GPU utilization, quality, reliability, and cost. Deploy and benchmark inference engines, build compression and quantization workflows, implement acceleration techniques, diagnose serving bottlenecks, and collaborate with kernel, platform, research, product, and customer teams.

Own the full lifecycle of machine learning features, including research, experimentation, training, deployment, monitoring, and production infrastructure. Build smart payment-routing models and MLOps practices, partner with product, data, engineering, and customers, evaluate outcomes statistically, mentor engineers, and help define ML technical direction and reliable development processes.

Own the full lifecycle of machine learning features, including research, experimentation, training, deployment, monitoring, and production optimization. Build ML infrastructure for payment routing, establish reliable MLOps practices, partner with product, data, engineering, and customers, mentor engineers, and guide technical priorities. Apply statistical thinking, A/B testing, pragmatic model evaluation, and reinforcement learning concepts in a remote-first environment.

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

Develop, run, validate, and interpret physics simulations for industrial and research problems. Investigate convergence, modeling assumptions, boundary conditions, mesh sensitivity, and discrepancies with reference data. Create reusable Python workflows, benchmarks, examples, and technical demonstrations. Collaborate with customers, application engineers, product teams, and solver developers to translate physical insight into product improvements. Depending on expertise, contribute to application engineering, simulation validation, scientific computing, numerical methods, or solver development.

Build end-to-end machine learning systems spanning research reproduction, model evaluation, inference infrastructure, backend services, and product interfaces. Design rigorous experiments, evaluation datasets, and verification workflows; analyze model internals; investigate provenance and evasion; and convert findings into reliable production systems with APIs, background jobs, observability, testing, documentation, and user-facing React/TypeScript experiences.

Lead new customer acquisition and strategic expansion across EMEA for Mirantis’ AI infrastructure solutions. Own the full sales cycle, develop territory strategies, build executive relationships, conduct technical discovery, design tailored infrastructure solutions, prepare statements of work, address technical objections, and negotiate complex commercial agreements. Partner with Sales Engineering and internal teams to develop business cases, influence product direction, and close enterprise SaaS and cloud infrastructure deals.

Design, build, and operate large-scale observability platforms covering metrics, logging, tracing, and alerting. Develop high-volume telemetry pipelines, implement SLO/SLI frameworks, improve incident detection and resolution, integrate incident-management workflows, and ensure platform reliability, scalability, security, and cost efficiency. Partner with operations teams, document architecture and runbooks, and contribute to AI-assisted operations capabilities.

Lead end-to-end architecture, implementation, and delivery of Blue Yonder Warehouse Management and related execution solutions. Serve as the primary client contact, manage timelines, budgets, risks, and deliverables, and lead solution teams and integrations. Provide functional and technical guidance, mentor junior staff, align solutions with business objectives, and collaborate with project managers, delivery partners, and stakeholders to deliver successful client outcomes.

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.

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.

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.

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.

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.