{"id":138928,"url":"https://alion.io/job/nomagic-research-scientist-2","title":"Research Scientist","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":["Zurich, Switzerland"],"countries":["CH"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":107000,"max_usd":248000,"period":"year","method":"role_country_seniority_unknown","sample_n":18},"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":"Knowledge Distillation","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Distillation","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Physical AI","optional":false},{"name":"Post-training","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"RLHF","optional":false},{"name":"Transformers","optional":false},{"name":"JavaScript","optional":true}],"status":"live","first_seen_at":"2026-05-14T06:43:10Z","employer_posted_date":"2026-05-14","last_verified_at":"2026-10-17T12:04:12Z","board_verified":true,"closed_at":null,"days_open":156,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":155},"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 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.\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.\n\nMuch 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.\n\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, not just offline metrics.\n\nHigh 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.\n\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.\n\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 this will be the first true proof-of-concept for scaled physical AI\n\nWhat you will do\nYour 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:\n\nFoundation Models & Pretraining\n\nDesign 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.\n\nMaster the Data: Develop scalable data mixtures and sampling strategies utilizing our massive offline repositories of vision, action, and state data.\n\nPush the Frontier: Run rigorous ablations to understand scaling laws, data quality effects, optimization dynamics, and large-model failure modes.\n\nScale with Engineering: Collaborate closely with ML Infra to push cluster utilization and throughput, ensuring our algorithmic ideas translate to efficient distributed training.\n\nAdaptation, Post-Training & Real-World Evaluation\n\nDrive 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.\n\nImprove 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.\n\nBuild Benchmarks: Design evaluation frameworks and lightweight physical setups that measure actual robot performance and failure modes far beyond the limits of simulation.\n\nClose 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.\n\nWhat skills we’d like you to have:\nExperience: Deep research and practical experience at the intersection of machine learning, systems engineering, and physical robotics.\n\nProven 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.\n\nEngineering 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.\n\nRobotics 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.\n\nPragmatic Research Mindset: You possess the ability to move seamlessly between theoretical design and physical implementation. 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