Research Engineer

Nomagic

Research Engineer focused on developing and deploying foundational models for physical AI using robotics and ML. Bridging the gap between research and industrial-scale execution in a hybrid environment.

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

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Robotics and Machine Learning, with a strong focus on large-scale multimodal model training and infrastructure. Capable of designing and implementing robust training stacks while effectively bridging research and practical application in real-world robotic systems.

Highest-signal resume keywords

Machine Learning ExpertiseRobotics Systems EngineeringPython ProficiencyDeep Learning Frameworks (PyTorch/JAX)Hands-On Hardware Experience

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Applicant Tracking System Keywords

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Hard Skills

Model TrainingFine-TuningDeep Learning ArchitecturesRobotics Full StackDebuggingInfrastructure DesignExperiment MonitoringData CollectionEvaluation MetricsDocumentation Writing

Soft Skills

Execution FocusIteration SpeedCollaborationProblem-Solving

Tools & Technologies

TransformersVLMsVLAsImitation LearningReinforcement Learning

Industry Keywords

Robotic TasksPhysical BenchmarksReal-World EvaluationOperational BottlenecksFeedback Loop

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

Deep experience and understanding at the intersection of machine learning, systems engineering, and robotics.
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.
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.
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.
You possess the ability to move seamlessly between research and implementation. You prefer execution, iteration speed, and real-world robustness over theoretical purity.

Benefits

Comp & perks

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