Research Engineer (Physical AI)

Location: Zurich, Switzerland; hybrid workplace

Company

An established robotics company is using production deployment logs and real-world robot operations to develop foundational models for physical AI.

What you will do

• Design, implement, and maintain infrastructure for large-scale VLA model training, including scheduling, distributed execution, job management, checkpointing, and logging.
• Build tools for launching, monitoring, debugging, reproducing, and analyzing complex ML experiments.
• Use offline data from classical robotics stacks and production deployments to pre-train robust robot foundation models.
• Design physical robotic benchmarks, configure and calibrate robots, coordinate data collection, and run structured on-robot evaluations.
• Analyze real-world evaluation results and identify software, hardware, and deployment bottlenecks.
• Test tools for teaching robots new skills and document reproducible workflows for the broader team.

Requirements

• Deep experience at the intersection of machine learning, systems engineering, and robotics.
• Experience training, fine-tuning, and deploying deep learning architectures such as Transformers, VLMs or VLAs, imitation learning, or reinforcement learning for robot control.
• Strong software engineering and infrastructure skills, with high proficiency in Python and PyTorch or JAX.
• Hands-on comfort with robotic hardware and understanding of perception, controls, and state estimation.
• Ability to move between ML research and implementation, prioritizing execution, iteration speed, and real-world robustness.
• English-speaking environment and willingness to work in a hybrid setup in Zurich.

Culture & Benefits

• Hands-on work with real robots solving production problems.
• Combination of advanced ML research and engineering applied to real-world robotics.
• Access to large-scale deployment data from robots operating in production.
• Testing through unit tests, simulations, and evaluations on real robots.
• Flexible working hours and a relocation package.
• Focused team environment with high-impact research and engineering work.

Hiring process

• Phone screen with the hiring manager.
• Half-day onsite process covering cultural fit and in-depth technical interviews.
• Final decision within 2–3 days after the onsite interview, with detailed feedback provided.