Top Machine Learning Jobs in Czechia

Owner of end-to-end ML systems for revenue management: design, deploy, and maintain production-grade models and data pipelines. Define ML testing, SLIs/SLOs, monitoring and drift detection. Collaborate with product and engineering, mentor engineers, and ensure scalable, reliable ML-powered pricing and revenue features for hotel customers.

Join the MLOps team to deploy, maintain, and monitor ML models in production. Build data and model pipelines, implement CI/CD for ML, optimize performance and cost, and collaborate with cross-functional teams to ensure scalable, reliable AI solutions.

Join a cross-functional team to design and implement AI agents and LLM-powered applications. Use GitHub Copilot and AI-assisted development, apply prompt engineering, integrate tools and agent workflows, and leverage technical documentation as knowledge sources. Deliver PoC work and contribute to SDLC improvements and best practices.

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.

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.

Provide strategic and operational EU-focused data privacy legal counsel across business functions. Advise on GDPR, EU Data Act, cross-border transfers, privacy program implementation, DPIAs, HR and marketing processing, M&A diligence, contracts, incident response, regulator interactions, and data governance for products and services including cloud and AI.

Build and own ML infrastructure for LLMs: observability, model serving, evaluation tooling, annotation and prompt platforms, cost and rate-limit management, security reviews, and hosting fine-tuned models to enable safe, scalable AI features.

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.

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.

Lead end-to-end design and delivery of production-grade LLM/agent systems embedded in enterprise products. Architect AI orchestration, integrate foundation models via secure APIs, optimize latency/cost/reliability, and implement Responsible AI guardrails for sensitive HR and financial data. Drive experimentation, prototyping, and cross-functional product execution at scale.

Lead research and experimentation on AI/ML models (especially transformers) to detect and evaluate security incidents on terabytes of behavioral data, design production-ready solutions, mentor researchers, and collaborate with data, security operations, and engineering teams to drive AI applications across the incident lifecycle.

Design and build end-to-end ML systems for fraud detection: develop and deploy models, create production-ready features and real-time data pipelines, integrate with backend services, and ensure monitoring, security, and observability.

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.

Design, train, fine-tune, distill, and deploy LLM-based and agentic ML models. Build and operate agent workflows in Python, run evaluation and benchmarking, select models and cost strategies, and manage production monitoring and observability for model performance and reliability.

Own, design, build, and operate distributed, high-load backend services and data pipelines for ML infrastructure. Partner with ML and product teams to deliver self-service, reliable model serving, storage, and deployment components; ensure observability, scalability, and performance while driving technical decisions and continuous improvement.

Research and engineer large multimodal and reinforcement-learning models for robotic agents: design, train, evaluate models; prototype in simulation and on hardware; build data pipelines, simulation environments, and distributed training infrastructure; collaborate to scale promising approaches into production and publish results.

Design and build research prototypes and production-capable systems to enable portable, interoperable agent and model workflows. Develop model routing, provider abstractions, memory and context representations, benchmarking suites, and evaluation protocols. Run ML experiments, implement robust APIs and integration layers, collaborate across research and engineering, and communicate results via reports, demos, publications, and open-source releases.

Build and ship state-of-the-art speech systems end-to-end: model architecture, training (pre-train/fine-tune/post-train), evaluation, tooling, data curation, production inference, and safety/abuse mitigation for voice conversion and related tasks.

Build and operate production-grade model serving infrastructure, design deployment pipelines (blue/green, canary), implement autoscaling and multi-model serving, optimize GPU utilization and network throughput, set up observability and model registries, manage CI/CD for reproducible deployments, own full ML system lifecycle including on-call support and platform scalability.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

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.

Develop and ship features for Junie across CLI and model integrations, experiment with model setups and ensembling for cost-quality tradeoffs, build and test autonomous agent behaviors for long-term maintenance, and own projects from research to production while using Junie daily to iterate on quality and UX.

Design, train, and improve computer-vision and generative ML models; handle end-to-end ML pipeline from data collection to deployment; prototype research ideas and convert successful prototypes into product-ready solutions; optimize models for efficient on-device inference and collaborate with product teams to integrate models.

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.