Gen ai large behaviour model specialist

Location: Munich, Germany

Language: German (B2+ required) & English

Work Model: Hybrid (On-site hardware lab days)

Position Type: Full-time, Permanent

The Opportunity

On behalf of a highly innovative, well-funded pioneer in the German robotics ecosystem, we are seeking a visionary Generative AI / Large Behaviour Model (LBM) Specialist. Operating at the absolute frontier of Physical AI, this company is actively moving away from traditional, deterministic programming. Instead, they are building cutting-edge neural architectures designed to give intelligent physical agents the ability to perceive, reason, and autonomously execute complex manipulation tasks in unstructured environments.

Based in Munich, this organization combines the fast-paced agility of an elite deep-tech research lab with the robust engineering scaling infrastructure of an established industry disruptor. This position offers a rare hybrid balance: the flexibility of remote software development paired with critical hands-on hardware validation in a world-class robotics laboratory.

The Core Mission: Transitioning advanced generative models from the digital realm to physical reality. The successful candidate will spearhead the development of multi-modal architectures where high-level semantic intent seamlessly translates into zero-shot physical execution.

Key Responsibilities

• Architecture Development: Design, train, and fine-tune Vision-Language-Action (VLA) models and large behaviour Models (LBMs) tailored for low-latency, multi-modal robot control.
• Sim-to-Real Pipeline Optimization: Build and scale scalable training pipelines utilizing hyper-realistic simulation physics environments to train generative policies at scale before deploying models to physical hardware.
• End-to-End Learning Systems: Develop, test, and optimize end-to-end neural networks capable of directly mapping raw multi-modal sensor inputs (visual, tactile, spatial telemetry) into real-time, fluid motor commands.
• Cross-Functional Hardware Integration: Collaborate closely with Embedded Software Engineers and Control System Engineers to deploy, benchmark, and iterate on models running directly on edge compute units (e.g., NVIDIA Jetson platforms).
• State-of-the-Art Research Transition: Monitor, replicate, and adapt the latest machine learning breakthroughs (Diffusion Policies, Transformer-based architectures, and generative behaviour cloning) from top academic research into production-ready commercial frameworks.

Candidate Profile

The ideal candidate sits at the intersections of modern deep learning theory, generative AI architecture, and physical hardware deployment. They are inherently curious, analytical, and possess a deep passion for solving the unstructured, real-world edge cases of Embodied AI.

Required Qualifications & Skills

• Education: Master’s degree or PhD in Computer Science, Robotics, Machine Learning, Physics, or a highly quantitative field with a specialized focus on deep learning.
• Generative AI & Architecture Experience: Proven track record of developing and scaling Transformers, Diffusion Models, or large multi-modal foundational models. Specific experience adapting these models for spatial reasoning, navigation, or physical actions is highly advantageous.
• Core Tech Stack: Exceptional production-grade programming skills in Python and deep familiarity with machine learning frameworks such as PyTorch or JAX.
• Robotics Frameworks: Direct, hands-on experience or profound theoretical understanding of ROS2 (Robot Operating System), data serialization, and robotic kinematics.
• Simulation Tools: Prior experience with modern physics engines and simulation environments (e.g.,NVIDIA Isaac Sim, MuJoCo, Drake) for robot learning.