Research Engineer

Nomagic·Zurichmid

In short

→Ingeniero de investigación que construye modelos de IA física a partir de datos reales de robots en producción.
→Trabaja diariamente con robots reales, entrenando y evaluando modelos de IA en entornos industriales.
→Destaca por usar datos reales de operación como 'internet data' para entrenar modelos fundamentales de IA física.

English-speaking environment

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What they ask for

✓Experiencia en robótica o aprendizaje automático con enfoque en modelos multimodales.
✓Capacidad para construir infraestructura de entrenamiento a gran escala.
✓Habilidades en diseño y mantenimiento de pipelines de entrenamiento distribuidos.
✓Experiencia con evaluación física de modelos en entornos reales.
✓Capacidad para cerrar el bucle entre investigación, ingeniería y operaciones.
✓Inglés avanzado para comunicación en entorno internacional.

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

RobóticaAprendizaje automáticoModelos multimodalesEntrenamiento a gran escalaInfraestructura distribuidaGestión de trabajosControl de versionesSimulaciónEvaluación físicaAnálisis de datos

Who should you write to at Nomagic?

Your free dossier identifies the people who'd interview you — their background, what they value, and how to reach out so you stand out before applying.

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 aren't 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. We’re 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 aren't 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.

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