Top Machine Learning Jobs in Poland

The Data Scientist will design advanced algorithms for generative AI models, analyze large datasets, and collaborate on AI advancements to enhance business processes.

Develops and scales predictive analytics and machine learning models for commercial decision-making. Responsibilities include forecasting, pricing optimization, profitability and tender analytics, customer behavior modeling, opportunity identification, model lifecycle management, and embedding analytical solutions into business processes. Partners with Commercial, Marketing, Finance, and IT stakeholders to translate business needs into scalable, interpretable insights and measurable growth outcomes.

Build and scale MLOps infrastructure that moves machine learning models from development to production. Responsibilities include model serving, automated training and deployment, CI/CD, monitoring, drift detection, rollback mechanisms, streaming feature pipelines, and agentic workflow integration. The role requires collaboration with data science, platform, and product teams to deliver reliable, observable, low-latency ML systems.

Build and enhance RAG systems, AI components, backend services, data pipelines, REST APIs, evaluation harnesses, and production AWS deployments. Write tests, investigate model failure modes, support containerized CI/CD releases, document solutions, and contribute to architecture and client handoffs. The role offers senior guidance while progressively increasing ownership of technical decisions and system components.

Build, operate, and improve production services that deliver machine learning models within exocad’s AI Design product. Responsibilities include model serving APIs, containerized deployments, versioning, staged rollouts, rollback automation, infrastructure provisioning with Terraform and AWS, monitoring, observability, and performance tuning. The role also contributes to training and labeling infrastructure and requires collaboration across engineering, product, and global teams, including work across AWS global and China environments.

The Ecosystem Evangelist will engage with partners and developer communities, support ecosystem initiatives, lead generation, and contribute to marketing activities.

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.

Define and execute the product roadmap for AI-driven blockchain security monitoring tools. Collaborate with cross-functional teams, analyze market and customer feedback, deliver features on time, and communicate with stakeholders to align expectations and improve product offerings.

Develop and improve Sophea AI’s large language models, including pre-training, fine-tuning, evaluation, inference optimization, and production deployment. Build ML pipelines for serving, monitoring, and lifecycle management while optimizing latency, throughput, cost, quantization, and GPU utilization. Work with multilingual datasets and benchmarks, with a particular focus on Polish-language models. The role requires strong hands-on LLM engineering experience and production ML systems expertise.

Develop AI and machine learning solutions for ADAS and automated driving systems. Apply computer vision, deep learning, multimodal learning, foundation models, and temporal modeling to automotive datasets and production applications. Implement, train, and evaluate models, translate research into practical systems, and collaborate with researchers and engineers. Work may involve perception, prediction, planning, autonomous systems, model optimization, and deployment for customer vehicles.

As a Technology Innovation Engineer, you will develop innovative platform solutions, evaluate emerging technologies, define integration strategies, and collaborate with stakeholders.

Design software architectures, implement new web application features with Django, collaborate with backend engineers and designers, own implementation through deployment, contribute product ideas, and work on Generative AI projects.

The Customer Success Manager will bridge enterprise customers with internal teams, ensuring platform integration and driving account growth through strategic customer engagement and technical guidance.

Own Go-To-Market for performance media buyers: build relationships, close and onboard clients, represent Kayzen at industry events, advise prospects on product strategy, and funnel market feedback across teams.

The Fund Accounting Director leads NAV processes, manages accounting teams, ensures timely delivery, and collaborates with clients and internal departments while resolving issues and enhancing procedures.

Lead and deliver Discrete Event Simulation (FlexSim) consulting engagements from discovery to implementation. Build, validate and present simulation models, support technical escalations, drive pre-sales demos and solution scoping, mentor junior staff, develop repeatable methodologies, and collaborate with product, development and customer success teams to improve operations and adoption across EMEA.

Lead full-cycle recruitment for AI/ML roles across Europe and LATAM: build pipelines, partner with hiring managers, screen candidates, manage interviews and offers, and improve AI-focused hiring processes.

Lead end-to-end Data & AI engagements: define objectives, shape architecture, manage delivery and teams, stay hands-on in implementation, and help grow accounts and pre-sales.

Design and deploy production-grade systems integrating machine learning models into scalable software pipelines, while focusing on maintainability, performance, and collaboration with teams.

Develops and deploys scalable AI and machine learning systems for precision oncology. Responsibilities include building multimodal data platforms and pipelines, model-training workflows, inference services, MLOps processes, APIs, and production software. The role applies foundation models, LLMs, generative AI, and vision-language models to clinical and research data while collaborating with scientists, clinicians, and informaticians to improve cancer research and patient outcomes.

Lead a Special Projects engineering team to explore and implement Generative AI and cloud solutions. Hands-on development in Python/Java, collaborate with product and data science, design and deploy ML/LLM solutions to production on GCP/Vertex AI, and mentor engineers while promoting continuous learning.

Senior Software Engineer will build and own backend-focused fullstack features across AI Retrieval, Infrastructure, or Product teams. Responsibilities include improving search/retrieval quality and latency, building scalable backend systems, contributing to embeddings/ML-powered search, collaborating cross-functionally, and shipping production-ready code in a continuous deployment environment.

Design, build, deploy, and optimize production-grade machine learning systems for a proactive AI platform. Own projects end to end, including data curation, training, evaluation, inference, monitoring, troubleshooting, and iteration. Translate research into reliable systems, optimize latency and cost at scale, collaborate across research, engineering, and product, and mentor other engineers.

Define and govern data architecture for global engineering platforms: design canonical data models, integration patterns, governed data products, metadata, lineage and quality standards, and enable AI/Generative AI use cases (knowledge graphs, vector search, RAG). Partner with architects, platform owners, and analytics teams, lead architecture reviews, and produce standards, blueprints, and implementation guidance to improve engineering analytics and platform adoption.

Enhance and operate a globally deployed recommender system by improving MLOps, CI/CD, experiment tracking, model deployment, scalable ML pipelines, monitoring, and observability. Provide technical guidance and mentorship while collaborating with data scientists, engineers, product managers, and business stakeholders to deliver reliable, reproducible, production-ready machine learning systems.