{"id":68945,"url":"https://alion.io/job/nomagic-research-engineer","title":"Research Engineer","company":{"id":28062,"name":"Nomagic","domain":"nomagic.ai","url":"https://alion.io/company/nomagic","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":89,"open_postings":10,"ghost_share":0.1,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":77,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Warsaw, Poland"],"countries":["PL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":62000,"max_usd":112000,"period":"year","method":"role_country_seniority_unknown","sample_n":268},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Bootstrap","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Imitation Learning","optional":false},{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Physical AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Transformers","optional":false},{"name":"Vision-Language-Action","optional":false},{"name":"JavaScript","optional":true}],"status":"live","first_seen_at":"2026-07-24T08:20:15Z","employer_posted_date":"2026-07-24","last_verified_at":"2026-10-17T05:39:35Z","board_verified":true,"closed_at":null,"days_open":85,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":84},"description":"Do you believe the path to general-purpose physical AI runs through noisy, real-world factory deployments?\nAre you excited by the challenge of turning the classical robotic stacks into the foundational training data for physical AI?\nDo you want to bridge the gap between world-class ML research and industrial-scale robotic execution?\nIf your answers are yes, we should talk.\nAt 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.\nOffer essentials\nPlay with real robots, solving real problems, every day.\nRelocation package.\nFlexible working hours.\nEnglish-speaking environment.\nHere is why we love this job ourselves, and hope you will enjoy it too:\nWe combine world-class research with top-notch engineering and apply it to solve real problems\nThe 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.\nWe 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.\nHigh 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.\nWorld-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.\nWe 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.\nWhat you will do\nYour 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.\nExpect challenges across two main pillars with the opportunity to specialise:Core Research & Large-Scale Infrastructure\nOwn 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.\nEnable Rapid Iteration: Build the critical tools and abstractions necessary for launching, monitoring, debugging, and seamlessly reproducing complex, multi-variant experiments.\nTrain from Deployment Logs: Utilize our massive repository of offline, classical stack data to pre-train robust robot foundation models.\nDrive 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\nReal-World Evaluation & Operations\nDesign Physical Benchmarks: Design new robotic tasks and build lightweight physical setups to systematically evaluate model capabilities far beyond the limits of simulation.\nExecute 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.\nClose 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.\nScale 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.\n\nWhat skills we’d like you to have:\nExperience: Deep experience and understanding at the intersection of machine learning, systems engineering, and robotics.\n\nProven 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.\n\nEngineering 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.\n\nRobotics 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.\n\nPragmatic Research Mindset: You possess the ability to move seamlessly between research and implementation. 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