Research Scientist

Nomagic

Research Scientist focusing on ML research, robotics, and model training for practical AI applications. Collaborating on innovative robotic solutions leveraging real-world data in Warsaw.

Posted 7/24/2026full-timeWarsaw • 🇵🇱 PolandMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in machine learning, systems engineering, and physical robotics, with a strong focus on designing and fine-tuning large-scale deep learning architectures. Capable of bridging theoretical research with practical implementation, ensuring robust performance in real-world applications.

Highest-signal resume keywords

Large-Scale Deep Learning ArchitecturesMachine Learning Frameworks (PyTorch/JAX)Robotics Full Stack (Perception, Controls, State Estimation)Imitation Learning and Reinforcement LearningData Sampling Strategies

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningDeep LearningModel Architecture DesignData Mixture DevelopmentFine-Tuning TechniquesEvaluation FrameworksOptimization DynamicsScaling Laws AnalysisRobustness Improvement MethodsFailure Analysis

Soft Skills

Pragmatic Research MindsetHands-On Hardware ExperienceEmpirical RigorRapid IterationExecution Focus

Industry Keywords

Foundation ModelsMultimodal Model TrainingDistributed TrainingRoboticsReal-World Evaluation

Tech Stack

Tools & technologies

PyTorch

About the role

Key responsibilities & impact

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 generalizable 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 utilization 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 behavior 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: Analyze real-world evaluation results to guide the overarching research direction, seamlessly bridging the gap between foundation model outputs and physical-world outcomes.

Requirements

What you’ll need

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.

Benefits

Comp & perks

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