{"id":799261,"url":"https://alion.io/job/spaitial-research-scientist-robot-learning-vla-wam","title":"Research Scientist - Robot Learning (VLA / WAM)","company":{"id":688399,"name":"SpAItial","domain":"spaitial.ai","url":"https://alion.io/company/spaitial","size_band":"201-500","is_staffing_agency":false,"is_intermediary":false,"ats_vendor":"Ashby","truth_index":{"grade":"C","score":64,"open_postings":10,"ghost_share":0.6,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-09-23T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom","Munich, Germany"],"countries":["GB","DE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":102000,"max_usd":265000,"period":"year","method":"role_country_seniority_unknown","sample_n":101},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Computer Vision","optional":false},{"name":"Fine-tuning","optional":false},{"name":"FSDP","optional":false},{"name":"Imitation Learning","optional":false},{"name":"Machine Learning","optional":false},{"name":"Post-training","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"SFT","optional":false},{"name":"Sim-to-Real","optional":false},{"name":"Tokenization","optional":false},{"name":"Vision-Language-Action","optional":false},{"name":"VLM","optional":false},{"name":"World Models","optional":false}],"status":"live","first_seen_at":"2026-08-20T11:57:49Z","employer_posted_date":"2026-08-20","last_verified_at":"2026-09-23T12:41:38Z","board_verified":true,"closed_at":null,"days_open":34,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":33},"description":"SpAItial is pioneering the next generation of World Models, pushing the boundaries of generative AI, computer vision, and the simulation of reality. We are moving beyond 2D pixels to build models that natively understand the physics and geometry of our world. Our mission is to redefine how industries, from robotics and AR/VR to gaming and cinema, generate and interact with physically-grounded 3D environments.\nWe're seeking aResearch Scientist to train the policies that turn a world model into a robot that acts. You will own vision-language-action (VLA) and world-action models (WAM) end to end, starting, including data, backbone, action representation, training runs, and the evaluation that tells us whether a policy is genuinely competent or merely lucky. A world model that understands geometry and physics still doesn't act on its own; the policy is what closes that gap. This is a senior, hands-on research role for someone who has already trained manipulation policies that worked, and who can say precisely why the ones that didn't failed.\nResponsibilities\nOwn the training pipeline for vision-language-action (VLA) and world-action models (WAM) end to end, from data to a policy running on a robot.\n\nContribute to setting the technical direction for embodied research at SpAItial.\n\nClose the sim-to-real gap through domain randomization, system identification, and calibration, and build evaluation that predicts real-world transfer.\n\nAdapt VLM backbones for control: encoder choice and adapter strategies, co-training.\n\nCurate and weight the training mix across heterogeneous robot datasets, spanning differing embodiments, action spaces, and sensor setups.\n\nDesign action representation and decoding, including tokenization, chunking, diffusion, and flow-matching action experts.\n\nBuild the world-model components that predict future observations conditioned on action.\n\nRun post-training: supervised fine-tuning onto target embodiments, and RL for robustness beyond demonstrations.\n\nKey Qualifications\nA PhD in robotics, machine learning, or computer vision with a robot learning focus, from the PhD alone or followed by industry experience.\n\nPublications at top venues such as (CoRL, RSS, ICRA, IROS or CVPR, ICCV, ECCV, NeurIPS), open-source work, and/or deployed systems.\n\nDeep experience with modern robot policy designs (VLA, WAM, diffusion), trained end to end rather than fine-tuned from a released checkpoint.\n\nStrong imitation learning fundamentals, and familiarity with RL fine-tuning of pretrained policies.\n\nFluency with VLM backbones and how to adapt them for control.\n\nExpert Python and PyTorch, with multi-node distributed training experience (FSDP or equivalent).\n\nAt SpAItial, we are committed to creating a diverse and inclusive workplace. We welcome applications from people of all backgrounds, experiences, and perspectives. We are an equal opportunity employer and ensure all candidates are treated fairly throughout the recruitment process.","description_format":"text","description_chars":2980,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","LLM & Generative AI","Foundation Models"],"lifecycle":[{"event":"open","at":"2026-09-12T06:56:27Z"}],"liveness":{"score":22,"band":"cold","label":"Long shot","p_open":1,"p_active":0.397,"p_room":0.55,"age_days":33,"expected_fill_days":32,"reasons":["conf:1","stale_co","win:tail"],"computed_at":"2026-09-23T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/spaitial-research-scientist-robot-learning-vla-wam","json_url":"https://alion.io/job/spaitial-research-scientist-robot-learning-vla-wam.json","meta":{"generated_at":"2026-09-23T14:41:10Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers"}}