Top Machine Learning Jobs in Munich

Work on Zendesk Explore to build and annotate golden datasets, implement LLM-as-Judge evaluation logic in Python within the Braintrust platform, and iterate automated metrics against human feedback to improve AI agent quality. Contribute across front-end and back-end of the SaaS product.

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.

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 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.

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.

Lead and grow a ~50-person ML organization, prioritize high-impact AI projects, set ML technical direction and standards, ensure delivery and measurable business value, and collaborate across products to translate AI research into product innovations.

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.

Design, develop, deploy, and maintain production-ready ML models and optimization algorithms for demand forecasting, shift planning, and staffing. Build MLOps pipelines, data pipelines and monitoring; collaborate with Workforce Management to translate operational problems into data-driven solutions.

As an AI Research Engineer at Helsing, you'll architect and enhance deep learning frameworks, optimize infrastructure for distributed training, and develop strategies for large datasets while collaborating with a cross-functional team.

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

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.

Build and scale ML-powered search and RAG capabilities at Zendesk scale. Collaborate with product, ML scientists, and engineers to design, deploy, and maintain reliable production services, improve relevance and ranking, optimize indexing pipelines, and mentor teammates while applying experiments and analysis to drive decisions.

Own and evolve ClickHouse's Python connector and SDK ecosystem, build integrations with orchestration and transformation tools, drive AI/LLM integration strategy for RAG and feature pipelines, engage with open-source community, and collaborate across product and engineering to prioritize real-world data workflows.

Lead research and development of AI accelerator/NPU IPs for embedded products. Architect RTL and hardware to meet power/performance/area targets, collaborate with compiler and software teams, model systems, and mentor cross-functional teams toward chip tape-out.

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.

The Data Engineer role focuses on developing predictive models and enhancing data engineering practices to support business objectives. Responsibilities include writing clean code, model evaluation, and collaboration with a global team to improve reporting and campaign management.

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.

Develop, train, evaluate, and iterate CV and 3D perception models for ADAS. Build scalable data pipelines (including auto/pseudo-labelling), improve detection/segmentation/tracking, and own end-to-end dataset generation and evaluation to improve real-world driving performance.

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

Lead end-to-end face ML projects: define data and labels, train and optimize deep-learning and classical CV models under latency/memory limits, build evaluation and monitoring pipelines, investigate failures, and ship production-quality solutions while collaborating across mobile, infra, and product teams.

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.

Lead development and evolution of end-to-end ML pipelines, focusing on integration, orchestration, automation, reproducibility, and model serving to improve forecasting and dynamic pricing. Shape ML architecture, drive data-driven decisions, and collaborate across multidisciplinary, international teams.