Research Scientist

Nomagic; Warszawa; On-site; PLN 200,000 - 320,000; Full time

Benefits offered by this job

Relocation package

Flexible working hours

English-speaking environment

Play with real robots

Job summary

Nomagic in Warsaw is seeking a Research Scientist to help build, train, and deploy foundational multimodal models that transform robotic fleets from classical control to generalized AI.

The role blends ML research with real-world robotics, offering hands-on work with production data and robots, plus relocation and flexible hours in an English-speaking environment.

Qualifications

• Deep research and practical experience at the intersection of machine learning, systems engineering, and physical robotics.
• Experience designing, training, and fine-tuning large-scale deep learning architectures (VLMs, VLAs, RL, RLHF, Imitation Learning), deployed on hardware.
• Engineering fundamentals in DL frameworks; debugging across the stack; emphasis on empirical rigor and robustness.

Responsibilities

• Define model architectures and curricula for multimodal robotic data.
• Develop scalable data mixtures from vision, action, and state logs.
• Perform ablations to study scaling laws, data quality effects, and optimization dynamics.
• Collaborate with ML Infra to optimize distributed training.

Skills

Deep research experience

ML, systems engineering, robotics

Large-scale DL architectures (VLMs, VL

PyTorch/JAX

Robotics intuition

Tools

PyTorch

JAX

Job description

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 Scientists 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.
• Much of this 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, not just offline metrics.
• High leverage, high impact. We’re still a highly focused team. If your architectures and training curricula 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 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 ML research, robotics, and large-scale multimodal model training. Expect challenges across two main pillars, with the opportunity to specialize in Pretraining or Post-training:
• Foundation Models & Pretraining
  • Design the Base Intelligence: Define model architectures (Transformer- and Diffusion-based), objectives, and training curricula across multimodal robotic data, turning raw deployment logs into generalisable capabilities.
  • Master the Data: Develop scalable data mixtures and sampling strategies utilizing our massive offline repositories of vision, action, and state data.
  • Push the Frontier: Run rigorous ablations to understand scaling laws, data quality effects, optimization dynamics, and large-model failure modes.
  • Scale with Engineering: Collaborate closely with ML Infra to push cluster utilisation and throughput, ensuring our algorithmic ideas translate to efficient distributed training.
• Adaptation, Post-Training & Real-World Evaluation
  • Drive Downstream Adaptation: Explore fine-tuning recipes to make general models – our own as well as our partner’s models – useful, controllable, and safe in the real world using imitation and reinforcement learning, distillation, and curriculum learning.
  • Improve Physical Robustness: Develop cutting-edge methods for improving real-world reliability, handling out-of-distribution edge cases, and steering robot behaviour in mature factory environments.
  • Build Benchmarks: Design evaluation frameworks and lightweight physical setups that measure actual robot performance and failure modes far beyond the limits of simulation.
  • Close the Physical Loop: Analyse real-world evaluation results to guide the overarching research direction, seamlessly bridging the gap between foundation model outputs and physical-world outcomes.

What skills we’d like you to have:

• Experience: Deep research and practical experience at the intersection of machine learning, systems engineering, and physical robotics.
• Proven Track Record: Experience designing, training, and fine-tuning large-scale deep learning architectures (VLMs, VLAs, RL, RLHF, Imitation Learning), ideally with policies deployed and validated on real hardware.
• Engineering Excellence: Strong deep learning framework fundamentals (PyTorch/JAX). You are comfortable debugging at every layer of the stack and care about empirical rigor as much as raw iteration speed.
• Robotics Intuition: Comfort working hands-on with hardware. You understand the robotics full stack (perception, controls, state estimation) and care deeply about evaluation and failure analysis when software meets the physical world.
• Pragmatic Research Mindset: You possess the ability to move seamlessly between theoretical design and physical implementation. You prefer execution, rapid iteration loops, and real-world robustness over academic 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

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analysing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgement. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.