Top Machine Learning Jobs in Czechia

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.

The Customer Value Architect will engage with customers to ensure the effective adoption and value realization of the Neara platform, focusing on ROI metrics and customer relationships.

Design and optimize probabilistic ML models for esports, build and maintain data pipelines, implement MLOps and CI/CD for automated training and deployment, collaborate with data scientists and engineers, and communicate model insights across teams.

Lead two ML teams across esports verticals, owning end-to-end ML lifecycle, roadmap, delivery, and MLOps. Act as technical bridge to product and infra, unblock teams, drive hiring and performance, introduce AI tooling, and ensure scalable, validated production deployments while managing cross-team dependencies and agile execution.

Student intern will research and implement ML/LLM-based solutions for email threat detection, incident similarity, and LLM feature improvements. Work includes designing models, building scalable pipelines, collaborating with engineers, evaluating performance, and communicating results via reports and potential publications.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

The role involves R&D of ISP technology, algorithm development, HW/SW co-design, sensor tuning, and customer support for image processing solutions.

Lead end-to-end technical programs for ML infrastructure (inference, serving, efficiency, endpoints). Coordinate cross-functional teams, prioritize overlapping projects, manage timelines, budgets and scope, drive incident management and continuous improvement, and communicate status to engineering and leadership.

Design, build, and operate ML platform systems to train, evaluate, deploy, and serve generative models. Improve reliability, scalability, observability, and cost-efficiency of GPU and cloud workloads. Build internal tools and agent-friendly workflows, collaborate with researchers and engineers, and drive platform architecture and automation.

Design, build and scale an automated translation-quality evaluation framework for localisation and dubbing. Implement production ML evaluation infrastructure, roll it out across teams, align metrics with product signals, and maintain/iterate systems in collaboration with R&D and cross-functional partners.

Build and own ML infrastructure for LLMs including observability, model serving, evaluation tooling, annotation systems, prompt platform enablement, cost optimization, security reviews, and ensuring stable, scalable LLM production integrations.

As a Senior Software Engineer in AI, you'll build and maintain ML features, collaborate with data scientists, and ensure quality and performance in a production environment within a SaaS setup.

As a UX Writer at Nebius, you'll improve user experience by creating UI copy, collaborating with teams, and developing guidelines.

Lead customer engagements to design and deliver production-ready data, ML and Generative AI solutions on Google Cloud. Run architecture and GenAI workshops, create solution and implementation roadmaps, build end-to-end data and AI pipelines, establish MLOps/LLMOps practices, advise on LLM integration/RAG/prompt engineering, support pre-sales, mentor junior engineers, and contribute reusable frameworks and offerings.

Design and implement ML models for personalization and predictions. Analyze data, research ML techniques, and collaborate with ML engineers for production integration.

As a Machine Learning Engineer, you'll build analytical tools in Python, improve data processing infrastructure, deploy models, and create dashboards. You'll collaborate closely with teams and gain feedback on your code delivery.

Lead ML infrastructure upgrades in hyperscale data centres across EMEA: manage contractors, schedules, logistics, mechanical/electrical/fibre coordination, commissioning (L3/L4 support), risk mitigation, reporting, and lessons learned throughout full project lifecycle.

The Machine Learning Researcher will explore novel approaches for threat detection through data analysis, designing experiments, deploying models, and collaborating across teams in a dynamic research environment.

Build ML prototypes and data pipelines, collaborate with researchers and product engineering to integrate models into Fusion, containerize and deploy applications, implement best practices, and document architectures to accelerate research-to-production workflows.

The ML Engineer will develop and enhance predictive models, evaluate their performance, document research, and maintain existing products in a collaborative environment.

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.

The Senior Data Engineer will design and build data pipelines, integrate ML models, optimize performance, and ensure data quality, while collaborating with traders and data scientists.

The Machine Learning Engineer will analyze large-scale marketing data, build and improve machine learning models, and collaborate with teams to enhance business decision-making.

Lead end-to-end ML systems for LLM/NLP document intelligence and supply-chain predictive analytics. Design and fine-tune transformer-based models, implement RAG pipelines, build forecasting, classification and optimization algorithms, and productionize models using containerization and MLOps tools. Ensure robust, explainable models deployed to secure on-premise environments with monitoring and continuous improvement.

Design, develop, and maintain high-performance trading platforms and risk systems by collaborating with traders and stakeholders, implementing features, and driving innovation.