Research Engineer

Role Overview

• Your focus will be defined by the intersection of Robotics and ML and large-scale multimodal model training
• expertise in both is optimal and alternatively eagerness to learn.
• Expect challenges across two main pillars with the opportunity to specialise:
• Core Research & Large-Scale Infrastructure.
• Own the Training Stack: Design, implement, and maintain the core infrastructure for large-scale VLA model training, including scheduling, distribution, job management, checkpointing, and rigorous logging.
• Enable Rapid Iteration: Build the critical tools and abstractions necessary for launching, monitoring, debugging, and seamlessly reproducing complex, multi-variant experiments.
• Train from Deployment Logs: Utilize our massive repository of offline, classical stack data to pre-train robust robot foundation models.
• Drive the Software Feedback Loop: Translate core research needs into concrete infra capabilities, track experiments, analyze results, and close the loop directly with ML researchers to unblock model progress.
• Real-World Evaluation & Operations.
• Design Physical Benchmarks: Design new robotic tasks and build lightweight physical setups to systematically evaluate model capabilities far beyond the limits of simulation.
• Execute Structured Evaluations: Ensure robots are properly configured, calibrated, and ready for rollouts. You will coordinate data collection efforts and run structured, on-robot evaluations to measure real-world success rates.
• Close the Physical Feedback Loop: Analyze real-world evaluation results to guide the ML research direction. You will identify operational bottlenecks across software, hardware, and deployment systems to continuously improve our iteration speed.
• Scale the Workflows: Beta test internal and third-party tools for teaching robots new skills, and write clear, structured documentation so the broader team can reproduce your workflows and scale your impact.

Requirements

• Engineering or technical degree (or relevant experience in sales) combined with 5+ years of customer-facing experience.
• Extensive experience in automation and/or intralogistics, including ability to engage customers on solution designs.
• Successful experience as logistics consultant or key account manager is a strong plus.
• Very high empathy. You understand the untold and the small signals and act on them.
• Excellent English & German communication.
• Ability to travel up to 50% of the time.
• Ability to work in a fast changing scaleup environment.
• Experience: Deep experience and understanding at the intersection of machine learning, systems engineering, and robotics.
• Proven Track Record: Experience training, fine-tuning, and deploying modern deep learning architectures (Transformers, VLMs or VLAs, Imitation Learning, RL) for robot control, ideally with policies validated on real hardware.
• Engineering Excellence: Strong software engineering and infrastructure skills. You are highly proficient in Python and deep learning frameworks (PyTorch/JAX) and can write clean, scalable code for training and evaluation.
• Robotics Intuition: Comfort working hands-on with hardware. You understand the robotics full stack (perception, controls, state estimation) and how to debug failures when software meets the physical world.
• Pragmatic Research Mindset: You possess the ability to move seamlessly between research and implementation. You prefer execution, iteration speed, and real-world robustness over theoretical purity.

Tech Stack

• Python
• PyTorch

Benefits

• Play with real robots, solving real problems, every day.
• Relocation package.
• Flexible working hours.
• English-speaking environment.