Top Machine Learning Jobs in Munich

Lead the strategy, design, deployment, and scale-up of AI-powered health products across the Global South. Own product roadmaps, execution, government-system integration, stakeholder adoption, partnerships, grant development, and portfolio expansion. Collaborate with engineering, machine learning, program, and domain teams to deliver sustainable, measurable public-health impact.

Develop, analyze, and implement quantum error-correcting codes and decoding algorithms for fault-tolerant quantum computing. Design fault-tolerant operations (e.g., lattice surgery), implement and optimize decoders in Python/C/C++/Rust (potential FPGA deployment), perform resource estimation and scalability analysis, collaborate with hardware and software teams, and publish research in scientific venues.

As a Machine Learning Engineer, you'll integrate intelligent systems into healthcare, build and deploy ML models on clinical datasets, and work with federated learning frameworks.

Architect and build the AI layer for Neo4j’s Hume Maestro platform. Responsibilities include setting the AI technical roadmap, contributing hands-on to production code, designing scalable AI architectures, establishing model evaluation and deployment practices, integrating AI with front-end and platform systems, and collaborating with Product and Science stakeholders. The role may eventually evolve into leading and mentoring a dedicated AI engineering team.

Own full-cycle recruiting for technical roles, partner with hiring managers to define roles, source and close candidates, build scalable systems and pipelines, optimize hiring with data, and support a global, remote people team while acting as a culture ambassador.

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.

Assess and evaluate AI-generated technical content, craft and answer advanced ML/AI questions, identify errors and hallucinations, rank responses by correctness and clarity, and provide precise feedback to improve model performance.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.

Build end-to-end AI systems and agents for procurement workflows. Improve search/matching, build evaluations and data pipelines, implement structured outputs and guardrails, and support backend scalability and reliability in a fast-paced startup environment.

The Consulting Manager will lead AI transformation programs, work with executives on strategy, drive business growth, represent the company in discussions, and mentor teams.

Build and operate scalable ML infrastructure spanning training, evaluation, telemetry, model deployment, and edge-device rollouts. Develop CI/CD pipelines, secure APIs, governed datasets, model lineage systems, drift detection, monitoring, staged experimentation, and rollback capabilities across Orb devices and mobile applications. Partner with research, product, and firmware teams to deliver reproducible, observable, privacy-conscious production ML systems.

Lead AI and machine learning architecture, mentor teams, drive innovation, and evaluate tools for AI systems development across global teams.

Lead finance transformation and pre-sales solution architecture for Workday Financials. Design end-to-end multi-country FIN solutions, lead workshops, drive executive alignment, enable sales, and shape post-go-live roadmaps using AI/automation to modernize finance operations.

Research and investigate global fraud trends using BioCatch data and external sources. Produce data-driven insights, visualizations, written thought leadership, dashboards, and presentations. Build repositories and automate analysis workflows while applying fraud expertise and AI tools to validate findings and support sales and marketing.

As an AI Product Manager, you will define the product roadmap, collaborate with teams to launch AI features, and drive data-driven decisions.

Build production-grade ML and MLOps infrastructure for cloud-native environments. Responsibilities include orchestrating Kubeflow pipelines, training and fine-tuning GPU-based models and LLMs, tracking experiments with MLflow, developing Python tooling, processing large datasets, and maintaining CI/CD, testing, security, and clean-code standards within Kubernetes.

Build and scale MLOps infrastructure that moves machine learning models from development to production. Responsibilities include model serving, automated training and deployment, CI/CD, monitoring, drift detection, rollback mechanisms, streaming feature pipelines, and agentic workflow integration. The role requires collaboration with data science, platform, and product teams to deliver reliable, observable, low-latency ML systems.

Own end-to-end delivery of specific ADAS features (e.g., TFL, High Beam Assist, Blind Spot Monitoring). Define feature scope, set performance targets, align OEM requirements with internal roadmaps, collaborate with engineering/product/OEM teams, participate in testing/validation, and mitigate risks to ensure features meet specs and production readiness.

Define and execute the product roadmap for AI-driven blockchain security monitoring tools. Collaborate with cross-functional teams, analyze market and customer feedback, deliver features on time, and communicate with stakeholders to align expectations and improve product offerings.

Lead design and delivery of a greenfield Anti Financial Crime platform. Translate AML/AFC requirements into technical specs, own roadmap and architecture, collaborate with AML/AFC operations and cross-functional product and engineering teams, and drive scalable detection, prevention, and automation using data and ML-enabled solutions.

Lead the core biometric recognition technology team, managing the full lifecycle of ML systems focused on iris and face recognition while ensuring privacy and reliability at scale.

Lead technical presales engagements for an AI-driven commerce platform: run discovery sessions, design composable solution blueprints, build demos and prototypes, author technical RFP/RFI responses, and collaborate with GTM, Product, and Engineering to close enterprise deals across the DACH region.

Own Go-To-Market for performance media buyers: build relationships, close and onboard clients, represent Kayzen at industry events, advise prospects on product strategy, and funnel market feedback across teams.

Proxima Fusion seeks passionate candidates for unsolicited applications to contribute to innovative clean energy solutions and support the development of fusion technology.

Own end-to-end delivery of ADAS features (AEB, LKA, ESA, AES, BSM), align OEM requirements with internal roadmaps, specify features, collaborate with engineering and product teams, support testing/validation, mitigate risks, and maintain strategic feature roadmaps toward higher autonomy.