Top Machine Learning Jobs in Munich

Own and ship core ML subsystems end-to-end for a proactive long-horizon AI assistant: data prep, training, evaluation, inference, debugging production failures, iterating rapidly, and mentoring other engineers while meeting latency, reliability, and safety constraints.

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.

Seeking a Senior/Lead Machine Learning Engineer with expertise in inference optimization, model acceleration, and high-performance systems for developing real-time multimodal models.

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 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 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.

Build and operate ML infrastructure powering model training, evaluation, deployment, inference, observability, and continuous improvement. Improve reliability, scalability, latency, throughput, and cost efficiency. Provide tooling and pipelines enabling fast experimentation, reproducible ML workflows, benchmarking, monitoring, and platform primitives consumable by AI engineers and researchers.

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 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, 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.

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.

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.

As a Senior Python Engineer, you'll design and implement scalable solutions, collaborate across teams, and manage infrastructure on cloud platforms, ensuring high-quality deliverables.