Research Engineer

Nomagic; Zürich; Vor Ort; CHF 90.000 - 120.000; Vollzeit

Benefits dieser Stelle

Relocation package

Flexible working hours

English-speaking environment

Zusammenfassung

Nomagic in Zürich, Switzerland, is seeking a Research Engineer to bridge the gap between world-class ML research and real-world robotic execution. You will focus on large-scale multimodal model training, design infrastructure for training models, and evaluate robotic task capabilities. Candidates should have extensive experience in machine learning, systems engineering, and robotics, alongside strong Python programming skills. The role offers flexible working hours and a relocation package.

Qualifikationen

• Deep experience and understanding at the intersection of machine learning, systems engineering, and robotics.
• Experience training, fine-tuning, and deploying modern deep learning architectures for robot control.
• Strong software engineering and infrastructure skills, proficient in Python and deep learning frameworks.
• Comfort working hands-on with hardware and debugging software-physical interactions.
• Ability to move seamlessly between research and implementation, focusing on execution and robustness.

Aufgaben

• Focus on Robotics and ML and large-scale multimodal model training.
• Design, implement, and maintain core infrastructure for model training.
• Build tools for launching, monitoring, and debugging complex experiments.
• Design new robotic tasks to evaluate model capabilities.
• Analyze real-world evaluation results to guide ML research.

Kenntnisse

Machine learning

Robotics

Systems engineering

Software engineering

Python

Deep learning frameworks (PyTorch/JAX)

Imitation Learning

Reinforcement Learning (RL)

Jobbeschreibung

Do you believe the path to general‑purpose physical AI runs through noisy, real‑world factory deployments? Are you excited by the challenge of turning the classical robotic stacks into the foundational training data for physical AI? Do you want to bridge the gap between world‑class ML research and industrial‑scale robotic execution?

If your answers are yes, we should talk.

At Nomagic, we are executing a humble pivot for general‑purpose physical AI. We believe that physical AI is fundamentally a knowledge transfer problem – we are leveraging the "internet data" of robotics – massive deployment logs from real systems operating in production environments – to bootstrap our efforts. We are looking for Research Engineers who will help us to build, train, and deploy foundational models that bring our fleet from a classical control stack to generalized AI mastery.

Offer essentials

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

What you will do

• 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.

What skills we’d like you to have

• 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.