{"id":1987751,"url":"https://alion.io/job/maxinsights-embodied-ai-vla-research-engineer","title":"Embodied AI / VLA Research Engineer","company":{"id":2224833,"name":"Maxinsights","domain":"maxinsights.ai","url":"https://alion.io/company/maxinsights","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Santa Clara, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":119000,"max_usd":266000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":370},"experience_years_min":1,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Computer Vision","optional":false},{"name":"Data Augmentation","optional":false},{"name":"DeepSpeed","optional":false},{"name":"Embodied AI","optional":false},{"name":"FSDP","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Vision-Language-Action","optional":false},{"name":"World Models","optional":false}],"status":"live","first_seen_at":"2026-10-07T01:31:43Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-24T10:17:24Z","board_verified":true,"closed_at":null,"days_open":17,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":17},"description":"Job Description:\nPosition Overview\nIn this role, you will work on the research, training, optimization, and real-world deployment of embodied AI models, including Vision-Language-Action (VLA) models, World Models, and related robotics foundation models.\nYou will work across multimodal perception, robot control, long-horizon task planning, large-scale robot data, and model deployment. This is a highly hands-on role that involves both model development and real-world robotic system integration.\nKey Responsibilities\nEmbodied AI Model Development\nResearch and develop Vision-Language-Action (VLA), World Model, and other embodied AI foundation models.\n\nWork on areas including:\nRobotic manipulation\n\nMultimodal perception and control\n\nLong-horizon task planning\n\nVision-language-action reasoning\n\nRobot-environment interaction\n\nDevelop and optimize models for real-world robotic applications.\n\nTranslate research ideas into practical models and systems that can operate reliably on physical robots.\n\nFoundation Model Training & Optimization\nTrain and optimize embodied AI foundation models using large-scale real-robot datasets and egocentric human demonstration data.\n\nDesign approaches for incorporating multimodal inputs such as:\nVision\n\nForce\n\nTactile sensing\n\nProprioception\n\nOther robot and environmental signals\n\nDevelop and evaluate multimodal fusion architectures.\n\nConduct model training, evaluation, benchmarking, and performance optimization.\n\nAnalyze model performance and identify opportunities to improve training efficiency, generalization, and real-world performance.\n\nRobotics Data Pipeline\nBuild and improve large-scale robotics data pipelines for model training.\n\nDevelop processes for:\nData cleaning\n\nData filtering\n\nResampling\n\nData augmentation\n\nData quality evaluation\n\nDesign scalable data pipelines capable of supporting large volumes of robot and human demonstration data.\n\nWork closely with data and robotics teams to improve dataset quality and training efficiency.\n\nModel Deployment & Real-Robot Testing\nDeploy trained models to physical robot systems.\n\nPerform real-world robot debugging, testing, and performance optimization.\n\nDiagnose issues across models, sensors, software, and robotic hardware.\n\nIterate between model training and real-world testing to improve system performance.\n\nHelp ensure models operate reliably and consistently in real-world environments.\n\nQualifications\nRequired\n1+ years of relevant industry or research experience in machine learning, robotics, computer vision, embodied AI, or a related field.\n\nStrong understanding of deep learning and modern machine learning methods.\n\nExperience with PyTorch or similar deep learning frameworks.\n\nExperience training and evaluating machine learning models.\n\nStrong programming skills in Python and familiarity with relevant ML/robotics tooling.\n\nUnderstanding of multimodal learning, computer vision, robotics, or related areas.\n\nAbility to work in a fast-paced startup environment and take ownership of technical problems from research through implementation.\n\nStrong problem-solving and debugging skills.\n\nStrong Plus\nReal-World Robotics Deployment\nExperience deploying and debugging machine learning models on physical robot systems.\n\nAbility to bring models from development into real-world robotic environments.\n\nExperience troubleshooting and stabilizing robotic systems in production or experimental environments.\n\nMultimodal / VLA Models\nExperience working with force, tactile, or other multimodal sensing.\n\nExperience designing or training multimodal fusion models.\n\nHands-on experience with VLA models, including model design, training, evaluation, or real-world applications.\n\nExperience with robotic manipulation or embodied AI systems.\n\nLarge-Scale Distributed Training\nExperience with large-scale distributed model training.\n\nFamiliarity with DDP, DeepSpeed, FSDP, or similar distributed training frameworks.\n\nExperience optimizing training performance, GPU utilization, memory usage, or training throughput.\n\nExperience working with large-scale datasets and distributed data pipelines.\n\nIdeal Candidate\nWe are looking for an engineer who is excited about the intersection of foundation models and physical robotics.\nThe ideal candidate is:\nHands-on and comfortable moving between research, coding, experimentation, and real-world robot testing.\n\nInterested in solving problems that cannot be addressed through simulation or software alone.\n\nComfortable working with large-scale datasets and modern foundation-model architectures.\n\nAble to take ownership of a problem from data → training → evaluation → deployment → real-world iteration.\n\nComfortable working in an early-stage environment where priorities can move quickly.\n\nCurious about emerging VLA, World Model, and embodied AI research and able to translate new ideas into working systems.\n\nWhy Join MaxInsights?\nWork directly on embodied AI and robotics foundation models.\n\nWork with large-scale real-world robotics and human demonstration data.\n\nGain hands-on experience across the full AI development lifecycle, from data pipelines to real-robot deployment.\n\nWork in a fast-moving startup environment with significant ownership and technical autonomy.\n\nCollaborate with teams working at the forefront of robotics and foundation-model development.\n\nDefault Benefits:\nHealth insurance\n\nVision care\n\nDental coverage\n\n401(k)\n\nPaid holidays\n\nPTO (Paid Time Off)\n\nSick leave","description_format":"text","description_chars":5444,"description_truncated":false,"requirements":{"experience_years_min":1,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Health insurance"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Robotics AI","AI Training Data & Annotation"],"lifecycle":[{"event":"open","at":"2026-10-07T03:27:12Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 6","filings_12m":6,"filings_prev_12m":1,"green_card_filings_12m":0,"median_offered_wage_usd":162500,"route":null,"cap_exempt":false,"checked_at":"2026-10-15T11:24:14+00:00","sources":["US 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