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
Here is why we love this job ourselves, and hope you will enjoy it too:
What you will do
- 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
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- 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.
What should you expect once you apply?
- A phone screen with the hiring manager to discuss your background and our technical direction.
- A half-day of on-sites (cultural fit & deep-dive technical interviews).
- A final decision made within 2-3 days after the on-site interview.
- Important: Expect detailed, honest feedback after completing the process, regardless of our decision.