Research Engineer
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Job Location:
Zürich - Switzerland
Monthly Salary:
Not Disclosed
Posted on:
18 hours ago
Vacancies:
1 Vacancy
Job Summary
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.
Here is why we love this job ourselves and hope you will enjoy it too:
- We combine world-class research with top-notch engineering and apply it to solve real problems
- The data already exists. We have robots in production at scale. We arent waiting for datasets to be collected; the byproduct of our machines doing useful work is being created right now.
- We measure what matters. We test our code in unit tests simulations and directly on real robots. Grounding our models in deployment allows us to truly measure performance.
- High leverage high impact. Were still a highly focused team. If your training recipes improve our agents you directly change the economics of the company.
- World-class peers. Our team has built Google Warsaw unicorn startups led research in DeepMind tested rocket engines and worked at top companies like Nvidia and ByteDance. Now we are shaping the reality of Physical AI together.
- We are building the bridge. We arent a new startup looking for an application; we are an established player bootstrapping physical AI. We believe that this will be the first true proof-of-concept for scaled physical AI.
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 wed 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.
Required Experience:
IC
About Company
scroll Keep your warehouse always ON ON Intelligent Robotics for Item Handling Are you wondering how picking robots could fit your AutoStore™? Watch Video to see an example Partners who take end-to-end responsibility for item handling Nomagic provides services based on Intelligent Rob ... View more
