Top Machine Learning Jobs in Munich

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.

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

Design and scale predictive ML systems for VIS.X, taking end-to-end ownership of various high-impact ML initiatives and shaping the ML architecture.

Design, build, and deploy low-latency ML models and inference pipelines to optimize card authorization and routing. Own feature engineering, production MLOps (CI/CD, monitoring), A/B and shadow testing, and model performance monitoring to reduce false declines and maximize approval rates.

Design, build, and deploy scalable production ML systems for MRI imaging. Lead ML model training, optimization, deployment, monitoring, and engineering best practices while mentoring teams and aligning ML architecture with business goals.

As a Machine Learning Scientist at Nucs AI, you will develop innovative ML methods for medical image analysis, collaborate with clinicians, and contribute to clinical validation studies, impacting cancer diagnostics.

Design and prototype ML solutions to improve developer workflows (code completion, generation, agents, test generation). Apply or train open-source models, build reproducible training and evaluation pipelines, stay current with ML-for-code research, collaborate with colleagues, and provide mentorship.

Lead design and implementation of ML/LLM systems for ingestion, knowledge extraction, retrieval, and reasoning. Build datasets, metrics, pipelines, agentic retrieval and RAG solutions. Establish MLOps, orchestration, observability, and experiment tracking. Collaborate on research agenda, shape ML architecture, and hire and grow the ML team.

Design, build, and maintain MLOps tools, automation, and end-to-end ML pipelines. Support large-scale GPU clusters, implement monitoring/observability, streamline reproducible training and deployment, and collaborate with product and engineering teams to optimize ML workflows for scalability and cost-efficiency.

End-to-end delivery of ML-powered services, involving research, prototyping, production, and operations. Collaborate on product requirements while maintaining high quality and operational excellence.

Lead the Applied AI Team in developing and deploying ML systems for clinical care, mentoring engineers, and driving initiatives across the AI stack.

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 role involves building and maintaining ML and cloud infrastructure for training large-scale generative models. Responsibilities include designing GPU clusters, supporting distributed training, managing storage systems, and collaborating with researchers and engineers.

As an AI Research Engineer, you will develop signal processing solutions, collaborate across teams, and apply AI in real-world scenarios within defence.

The role involves deploying AI solutions, collaborating with clients on technical challenges, and contributing to open-source projects. You will also be involved in fine-tuning LLMs and providing pre-sales technical guidance.

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 Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

As a Machine Learning Engineer, you'll develop and improve ML models for geospatial analytics, collaborating with various teams to enhance performance and reliability of ML systems.

The role involves building and operating ML systems and infrastructure for large foundation models, data pipelines, and experiment orchestration in a collaborative environment.

As an Intern Machine Learning, you will support the integration of AI solutions in healthcare, collaborating with teams to build and deploy machine learning models in production environments.

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.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.