Top Artificial Intelligence Jobs in Munich

Lead EMEA go-to-market for Physical AI by sourcing executive-level pipeline, accelerating top accounts, building EMEA-specific playbooks, enabling regional sales, and converting lighthouse wins into sustained cloud consumption across robotics, autonomy, simulation, and related AI-native industries.

Define and own the internal AI-as-a-Platform roadmap, coordinate cross-functional teams to identify and implement AI use cases, ensure AI compliance and governance, support GTM for AI features, manage agile product processes, and use data and LLM outputs to evolve platform strategy.

Architect and build an AI-native automation and orchestration toolchain (fusing traditional tools with foundation models), enable and mentor early adopter teams, standardize practices across departments, drive infrastructure-as-code and CI/CD adoption, contribute to the IT/Sec operating system, and champion cross-departmental adoption of automation and AI platforms.

Design, build, and maintain production-grade AI/LLM services and APIs. Integrate LLM APIs and agentic workflows into cloud-native architectures on AWS, extend Terraform infrastructure and CI/CD, and implement testing and monitoring for reliable AI-enabled systems. Collaborate cross-functionally to bring AI features into product.

Build and maintain agentic, AI-enhanced frontend experiences across Scalable’s products. Work in a cross-functional AI tribe, own end-to-end features, drive architecture and technical discussions, write high-quality tests and documentation, and integrate AI/LLM-powered interfaces like chat and developer tooling.

Lead and grow a new AI Engineering & AI Platform team to productionize LLM-backed use cases. Provide technical direction for backend systems, cloud-native architectures, IaC, CI/CD, and agentic workflows. Deliver end-to-end AI solutions, shape internal AI platform capabilities (observability, governance, cost/rate management), collaborate with Data Science, Product, Security and Stakeholders, establish engineering best practices, and participate in recruiting and onboarding.

Design, build, and maintain an internal AI provisioning platform for LLM access, observability, governance, cost control, and secure, cloud-native agentic workflow integrations.

Design rigorous, deterministic advanced physics problems and provide complete verified solutions for AI training and evaluation. Create reproducible simulations or computational workflows (using Python and scientific libraries), document reasoning clearly, and ensure technical precision. Contribute to problem design that tests deep multi-step physical reasoning.

Lead and build Axelera's AI Infrastructure Systems division to deliver datacenter-class hardware from accelerator cards and OAM/UBB modules to 1U/2U racks. Own architecture, schematics, PCB/layout review, power/thermal/mechanical design, BMC/firmware, bring-up, validation, qualification, NPI, and ramp with CMs/ODMs. Align roadmaps with silicon, SDK, and product teams, engage customers and partners, set engineering processes, manage budget and vendor relationships, and mitigate technical risks through hands-on leadership.

Lead design, training, and evaluation of LLMs for translation and language-quality tasks; build robust evaluation frameworks; architect agentic multi-model workflows; own model development from data curation to production; run reproducible experiments; collaborate with product and engineering and mentor researchers.

The AI-First Full-Stack Engineer will lead full-stack development using Node.js and Next.js, integrating AI workflows and orchestrating Claude agent teams for efficient engineering processes, ensuring compliance with healthcare standards.

Own end-to-end product for a slice of Nebius AI compute platform: define strategy, APIs, and roadmaps; drive cross-team delivery; conduct structured discovery; measure adoption and platform outcomes; engage engineering as a technical peer; and close operational feedback loops to improve reliability and customer experience.

Lead development of an autonomous agentic AI scientist for drug discovery: architect multi-step reasoning agents, bridge AI and wet-lab experiment design, prototype to productionize agentic systems, publish research, and collaborate with biological experts.

The Senior Product Manager will own the strategic roadmap, lead market research, manage monetization strategies, and mentor product managers within an AI and EdTech environment.

Lead and scale full-funnel B2B demand generation using paid acquisition, ABM, field marketing, and AI-driven automation. Build and optimize landing pages, email sequences, funnels, and campaign performance to drive MQLs and pipeline. Collaborate with Sales, Content, and Design, run experiments, and measure growth metrics (CPL, CAC, conversion rates) while leveraging LLM agents and marketing automation to accelerate execution.

Author and refine a systems-engineering curriculum for experienced engineers: shape syllabus, define learning objectives, produce and fact-check lessons using AI tools, design hands-on projects and rubrics, and iterate with LX designers to meet milestone deliverables over a 4–6 month, ~15–20 hr/week engagement.

As a Founding AI Engineer, you will develop AI systems for construction efficiency, focusing on agent harness engineering and project memory integration, working directly with founders in a hybrid environment.

As a Senior Forward Deployed Engineer, you'll deliver AI solutions, engage with customers, and lead project lifecycles, including deployment and integration.

Lead the design and deployment of advanced AI algorithms for Battery Management Systems, focusing on edge AI and real-time monitoring, collaborating across teams to ensure compliance with automotive standards.

As an AI Research Engineer focused on AI Safety, you will develop techniques for evaluating AI products, enhance decision-making via uncertainty quantification, and ensure robustness against adversarial attacks.

Build and operate the end-to-end AI platform: backend services, APIs, data ingestion pipelines for vector and graph DBs, full-stack internal tooling, and lifecycle automation to deploy and maintain production ML/LLM systems while collaborating with software and data science teams.

Build and ship LLM- and agent-powered product experiences for North, designing interfaces, evals, observability, and feedback loops to improve reliability and user outcomes. Collaborate with modelling, product, design, and customers to turn failures and feedback into measurable product and model improvements.

Build and operate the internal AI platform used by 600+ developers: secure AI coding environments, AI-assisted CI and agentic AWS workflows. Own products end-to-end, improve developer experience, ensure reliability/observability/cost-awareness, participate in on-call, and capture reusable patterns and templates for broad adoption.

Lead applied research and system design for agent-native search: build multi-stage retrieval, ranking, and grounding methods for LLM-driven, iterative agent workflows; deploy high-throughput, low-latency production systems; define evaluation metrics; run experiments and mentor engineers.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.