{"id":181,"url":"https://alion.io/job/airoa-rd-014-intern-research-scientist-vlas","title":"R&D-014 Intern Research Scientist (VLAs)","company":{"id":322,"name":"AI Robot Association","domain":"airoa.org","url":"https://alion.io/company/airoa","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"D","score":52,"open_postings":46,"ghost_share":0.804,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-29T05:49:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"intern","employment_type":"internship","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Tokyo, Japan"],"countries":["JP"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Computer Vision","optional":false},{"name":"Embodied AI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"RLHF","optional":false},{"name":"Sim-to-Real","optional":false},{"name":"Vision-Language-Action","optional":false},{"name":"Isaac Sim","optional":true},{"name":"Model Predictive Control","optional":true},{"name":"MuJoCo","optional":true},{"name":"PyBullet","optional":true},{"name":"ROS","optional":true}],"status":"live","first_seen_at":"2026-01-12T00:00:00Z","employer_posted_date":"2026-01-12","last_verified_at":"2026-10-29T07:59:29Z","board_verified":true,"closed_at":null,"days_open":290,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":290},"description":"About AIRoA\nThe AI Robot Association (AIRoA) is launching a groundbreaking initiative: collecting one million hours of humanoid robot operation data with hundreds of robots, and leveraging it to train the world’s most powerful Vision-Language-Action (VLA) models.\nWhat makes AIRoA unique is not only the unprecedented scale of real-world data and humanoid platforms, but also our commitment to making everything open and accessible. We are building a shared “robot data ecosystem” where datasets, trained models, and benchmarks are available to everyone. Researchers around the world will be able to evaluate their models on standardized humanoid robots through our open evaluation platform.\nFor researchers, this means an opportunity to:\n- Work on fundamental challenges in robotics and AI: multimodal learning, tactile-rich manipulation, sim-to-real transfer, and large-scale benchmarking.\n- Access state-of-the-art infrastructure: hundreds of humanoid robots, GPU clusters, high-fidelity simulators, and a global-scale evaluation pipeline.\n- Collaborate with leading experts across academia and industry, and publish results that will shape the next decade of robotics.\n- Contribute to an initiative that will redefine the future of embodied AI-with all results made open to the world.\nAs we prepare for our official launch on October 1, 2025, we are assembling a world-class team ready to pioneer the next era of robotics.\nWe invite ambitious researchers and engineers to join us in this bold challenge to rewrite the history of robotics.\nJob Description\nIn this role, you will be responsible for:\n- Design and implement data preprocessing pipelines for multimodal robot datasets\n- Train VLA models using supervised learning, RL, fine-tuning, RLHF, and training from scratch\n- Develop and evaluate models in both simulation and on physical robots\n- Improve training robustness and efficiency through algorithmic innovation\n- Analyze model performance and propose enhancements based on empirical results\n- Deploy VLA models onto real humanoid and mobile robotic platforms\n- Publish research in top-tier conferences (e.g., NeurIPS, CoRL, CVPR)\nRequirements\nRequired Qualifications\n(All of the following qualifications must be met)\nMS or PhD in Computer Science, Electrical Engineering, or a related field.\nProven track record of achieving significant results as demonstrated by publications at leading conferences in Machine Learning (NeurIPS, ICML, ICLR), Robotics (ICRA, IROS, RSS, CoRL), and Computer Vision (CVPR, ICCV, ECCV)\nExperience with open-ended learning, reinforcement learning, and frontier methods for training LLMs/VLMs/VLAs such as RLHF and reward function design\nExperience working with simulators or real-world robots\nExperience with deep learning frameworks such as PyTorch\nPreferred Qualifications\nPhD or equivalent research experience in robot learning.\nPractical experience implementing advanced control strategies on hardware, including impedance control, adaptive control, force control, or MPC.\nExperience using tactile sensing for dexterous manipulation and contact-rich tasks.\nFamiliarity with simulation platforms and benchmarks (e.g., MuJoCo, PyBullet, Isaac Sim) for training and evaluation.\nStrong end-to-end system building and rapid prototyping skills\nExperience with robotics frameworks like ROS\nBenefits\nThere are currently no comparable projects in the world that collect data and develop foundation models on such a large scale. As mentioned above, this is one of Japan’s leading national projects, supported by a substantial investment of 20.5 billion yen from NEDO.\nThis position will play a crucial role in determining the success of the project. You will have broad discretion and responsibility, and we are confident that, if successful, you will gain both a great sense of achievement and the opportunity to make a meaningful contribution to society.\nFurthermore, we strongly encourage engineers to actively build their careers through this project-for example, by publishing research papers and engaging in academic activities.\n•Work location\nTokyo Ryutsu Center A Bldg. 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