Top Artificial Intelligence Jobs in Munich

Lead product direction and execution for major areas of BJAK's AI Neobank app. Define roadmap, priorities, standards and success metrics across multiple squads; guide PMs, designers and engineers; translate regulatory and operational constraints into product decisions; drive launch quality, post-launch learning and continuous improvement; work across insurance, payments, savings, investing and travel.

Founding go-to-market and sales lead for DACH and Nordics. Build regional pipeline from scratch, land pilots with government/defence customers, scope solutions with an engineer, and convert pilots into long-term enterprise contracts while shaping regional strategy and playbooks.

Lead hands-on robot training and deployment at NeuraGym: onboard users, design curricula, collect and annotate data, run model training and simulation, support live demos, troubleshoot learning failures, and feed insights into product and AI roadmaps.

Lead and grow an AI engineering team to convert partner domain expertise into deployable robotic skills. Manage feasibility, data collection, teleoperation, training pipelines, simulation, deployment, and validation. Provide hands-on technical support, coordinate with on-site robotics engineers and cross-functional teams, shape product roadmap, and define metrics to drive continuous improvement.

Lead design and implementation of multimodal learning systems that fuse vision, tactile, IMU, depth, language, and robot-state inputs to produce policies for dexterous hands. Build and maintain GPU-accelerated simulation and data pipelines, drive sim-to-real transfer, and translate research into engineering milestones while mentoring engineers.

Design and train multimodal, learning-based policies (vision, language, tactile, IMU) for multi-contact dexterous hand manipulation using imitation learning and deep RL, and collaborate with ML, robotics, and software teams to deploy policies on real hardware.

Lead and manage a hands-on team of data operators and interns in a physical robot training facility (NEURAGym). Optimize shop-floor processes, track KPIs, coordinate staffing for data collection campaigns, support hiring, and liaise with stakeholders and robotics/AI teams to resolve technical challenges and shape hiring and skills development.

Follow predefined scenarios to interact with and evaluate an AI voice assistant, assess response accuracy and relevance, document incorrect or unexpected behavior, record test results, and follow project guidelines. Provide feedback to improve the AI during a paid, full-time 3-week pilot with training and equipment provided.

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

Short internship to build internal AI and automation solutions for Product Management: develop Copilot-based workflows, create automations with n8n/Power Automate, design AI agents, build KPI dashboards, integrate systems via APIs, and create prototypes and proofs-of-concept.

Develop and optimize AI pipelines for workflow intelligence, experiment with models, and collaborate to integrate AI systems into workflows.

Build and scale Jimdo Companion: design agent orchestration, LLM-powered workflows, recommendation engines, APIs and full-stack features; enable AI platform capabilities, collaborate cross-functionally, and drive product-quality AI in production.

Design, build and ship native Android features for an AI-enabled neobank using Kotlin. Implement secure, performant user flows for payments, insurance and financial services, integrate REST APIs, improve app stability and performance, use analytics to iterate, and incorporate on-device AI where it improves the user experience.

Build and ship native Android features in Kotlin for an AI-enabled neobank app. Design clean user flows for payments, insurance and financial services, integrate backend APIs, improve performance and stability, use analytics and on-device ML (TensorFlow Lite) where it adds value.

The AI Engineering Intern will design, test, and deploy scalable AI systems, integrate models, and work on inference optimization, collaborating with data scientists on complex problems.

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

AI Engineers at QuantCo design, build, and implement applied AI systems, working with data scientists to optimize inference, develop scalable solutions, and integrate AI into enterprise operations.

The role involves delivering features across the CRM platform for distributors, using AI tools, handling fullstack development tasks, and collaborating closely with teams and customers.

The AI Engineer will develop and deploy AI applications, collaborate on the end-to-end pipeline, create backend services, and develop frontend interfaces.

Partner with Product, Design, Engineering, and Data to define KPIs and measurement for AI-powered experiences. Guide instrumentation, design and evaluate experiments, build dbt models, create dashboards, and translate analytics into product recommendations to shape AI product strategy and best practices.

Lead enterprise AI transformation by advising customers, designing agentic AI architectures, delivering rapid POCs, integrating CX/CRM systems, building evaluation and automated evaluation pipelines, and scaling responsible AI solutions while mentoring teams and codifying reusable frameworks.

The role involves leading design for AI-native products at Synthesia, focusing on improving interaction patterns and enhancing user experience in AI-driven workflows.

Lead Data, Analytics, AI, and GenAI presales activities for strategic enterprise opportunities. Facilitate discovery workshops, translate business needs into scalable architectures, lead RFP and RFI responses, and present solutions to executives. Design end-to-end AI and cloud architectures across Azure, GCP, Databricks, and open-source ecosystems. Advise on AI transformation use cases, roadmaps, governance, and implementation, while mentoring delivery teams and developing reusable frameworks, offerings, thought leadership, and customer success materials.

Develop and deploy production-ready generative AI applications using Python, LLMs, RAG, AWS Bedrock, and OpenAI APIs. Responsibilities include model customization, API integration, data preparation, performance tuning, production deployment, documentation, and cross-functional collaboration. The role focuses on applied engineering rather than research and requires experience integrating AI solutions into business applications.

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.