{"id":3368191,"url":"https://alion.io/job/td-synnex-physical-ai-engineer","title":"Physical AI Engineer","company":{"id":14275,"name":"TD SYNNEX","domain":"tdsynnex.com","url":"https://alion.io/company/td-synnex","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":82,"open_postings":100,"ghost_share":0,"stale_share":0.74,"repost_share":0,"time_to_fill_p50_days":56,"computed_at":"2026-11-01T06:10:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Clearwater, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":109000,"max_usd":243000,"period":"year","method":"role_country_seniority_unknown","sample_n":4425},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Human-in-the-Loop","optional":false},{"name":"Imitation Learning","optional":false},{"name":"Isaac Lab","optional":false},{"name":"Isaac Sim","optional":false},{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"NVIDIA Omniverse","optional":false},{"name":"Physical AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Sim-to-Real","optional":false},{"name":"Stable-Baselines3","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Accelerate","optional":true},{"name":"C++","optional":true},{"name":"Data Augmentation","optional":true},{"name":"Localization","optional":true},{"name":"MuJoCo","optional":true},{"name":"Multimodal AI","optional":true},{"name":"NVIDIA PhysX","optional":true},{"name":"ROS","optional":true},{"name":"Vision-Language-Action","optional":true}],"status":"live","first_seen_at":"2026-08-05T00:00:00Z","employer_posted_date":"2026-08-05","last_verified_at":"2026-11-01T05:29:21Z","board_verified":true,"closed_at":null,"days_open":88,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":88},"description":"Physical AI Engineer\nBuild the Future of Robotics with Physical AI\nWe are building real-world Physical AI systems where models interact with physical machines. This role is for a robotics engineer with a strong reinforcement learning (RL) mindset, someone who wants to train, evaluate, and deploy intelligent behaviors that emerge through interaction, not just perception, by building the virtual environments, generating the data that trains our models, and developing the policies that eventually run on real hardware.\nDay to day, you will design simulation environments, produce large volumes of labeled synthetic data, train and evaluate learned policies, and work with engineers across robotics, controls, and perception to close the sim-to-real gap. You will work hands-on with NVIDIA Omniverse, Isaac Sim, physics-based simulation, and foundation models. This is a builder role: fast iteration, scalable training, and direct transfer from simulation to physical robots.\nWhat You'll Do\nBuild and maintain high-fidelity, physics-accurate simulation environments in NVIDIA Omniverse and Isaac Sim for training, testing, and validating robotic systems.\n\nGenerate synthetic datasets at scale, including sensor and camera simulation, domain randomization, procedural scene variation, and automated annotation such as segmentation, depth, bounding boxes, and pose. You own dataset quality, versioning, and delivery.\n\nDesign and run reinforcement learning and imitation learning pipelines using simulation-generated and synthetic data.\n\nTrain and tune policies for control, planning, navigation, and manipulation, with emphasis on robustness and sim-to-real performance.\n\nDefine task curricula, reward functions, and evaluation benchmarks so policy performance is measured before it reaches hardware.\n\nModel sensors, actuators, and contact behavior, and debug simulation instability, non-physical behavior, and determinism issues.\n\nDrive simulation-to-real transfer through domain randomization, system identification, and validation on physical systems.\n\nBuild reusable tooling, APIs, and documentation so the broader team can stand up new environments and tasks without deep simulation expertise.\n\nIntegrate foundation models to support reasoning, task decomposition, and human-in-the-loop learning.\n\nRequirements\n5+ years in robotics, reinforcement learning, simulation, or applied machine learning. Degree in Robotics, Computer Science, or a related field, or equivalent hands-on experience.\n\nHands-on experience training robotic agents in simulation, on physical systems, or both.\n\nStrong background in reinforcement learning, imitation learning, or learning-based control, including domain randomization and curriculum learning.\n\nProven experience building simulation environments in NVIDIA Omniverse, Isaac Sim, or Isaac Lab, or comparable GPU-accelerated simulation platforms.\n\nDirect experience generating synthetic data for model training, including sensor simulation, annotation pipelines, and large-scale dataset generation.\n\nWorking knowledge of OpenUSD as a robotics engineer, including asset conversion into a simulation pipeline from formats such as URDF or MJCF.\n\nProduction experience with at least one RL library: RSL-RL, RL-Games, skrl, or Stable-Baselines3.\n\nStrong Python and the deep learning stack, such as PyTorch or JAX, with the ability to build and scale training pipelines beyond a single workstation.\n\nExperience applying or integrating foundation models into robotics or decision-making workflows.\n\nBuilder mindset with a track record of moving learning systems from experiment to deployment.\n\nNice to Have\nPhysX schemas and physics tuning.\n\nMuJoCo Playground, NVIDIA Warp, or Newton.\n\nOmniverse Replicator or comparable synthetic data generation frameworks.\n\nWorld foundation models used for data augmentation and photoreal domain transfer.\n\nVision language action models or multimodal policies.\n\nROS 2 or comparable robotics middleware, real-time systems, or physics engines.\n\nModel-free or model-based RL at scale, including distributed or cloud-scale training orchestration.\n\nTraining perception models such as detection, segmentation, or pose estimation on synthetic data.\n\nC++ alongside Python for real-time robotics systems.\n\nExperience operationalizing learned policies on physical robots in production environments.\n\nWork Environment\nRemote or hybrid, US-based, with periodic time onsite at our robotics facility.\n\nOccasional domestic and global travel.\n\nFlexible working hours aligned to experimentation and training cycles.\n\n--- This description is optimized to attract senior, hands-on AI robotics engineers with strong reinforcement learning and simulation expertise.\nAt TD SYNNEX, our values guide everything we do: Together, We Own It, We Dare to Go, We Grow and Win, and above all, We Do the Right Thing. These principles shape how we work with each other, our partners, and our communities as we drive innovation and create lasting impact.\nWhat’s In It For You?\nElective Benefits: Our programs are tailored to your country to best accommodate your lifestyle.\n Grow Your Career: Accelerate your path to success (and keep up with the future) with formal programs on leadership and professional development, and many more on-demand courses.\nElevate Your Personal Well-Being: Boost your financial, physical, and mental well-being through seminars, events, and our global Life Empowerment Assistance Program.\nDiversity, Equity & Inclusion: It’s not just a phrase to us; valuing every voice is how we succeed. Join us in celebrating our global diversity through inclusive education, meaningful peer-to-peer conversations, and equitable growth and development opportunities.\nMake the Most of our Global Organization: Network with other new co-workers within your first 30 days through our onboarding program.\nConnect with Your Community: Participate in internal, peer-led inclusive communities and activities, including business resource groups, local volunteering events, and more environmental and social initiatives.\nDon’t meet every single requirement? Apply anyway.\nAt TD SYNNEX, we’re proud to be recognized as a great place to work and a leader in the promotion and practice of diversity, equity and inclusion. If you’re excited about working for our company and believe you’re a good fit for this role, we encourage you to apply. You may be exactly the person we’re looking for!\nWe are an equal opportunity employer and committed to building a team that represents and empowers a variety of backgrounds, perspectives, and skills. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, gender, gender identity or expression, sexual orientation, protected veteran status, disability, genetics, age, or any other characteristic protected by law.\nTD SYNNEX is an E-Verify company","description_format":"text","description_chars":6893,"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":["Equity","Flexible schedule","Professional development"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Wholesale","Servers & Data Center Hardware","IT Resellers & VARs"],"lifecycle":[{"event":"open","at":"2026-10-27T23:54:18Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 6 · green card filings: 6","filings_12m":6,"filings_prev_12m":4,"green_card_filings_12m":6,"median_offered_wage_usd":119167,"route":null,"cap_exempt":false,"checked_at":"2026-10-29T16:27:21+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)","US Department of Labor: PERM disclosure data (green cards)"],"filings_for_role_12m":2}],"liveness":{"score":15,"band":"cold","label":"Long shot","p_open":1,"p_active":0.406,"p_room":0.36,"age_days":88,"expected_fill_days":56,"reasons":["conf:0","stale_co","velocity","win:tail","crowd:brand"],"computed_at":"2026-11-01T06:10:25Z"},"pay":null,"html_url":"https://alion.io/job/td-synnex-physical-ai-engineer","json_url":"https://alion.io/job/td-synnex-physical-ai-engineer.json","meta":{"generated_at":"2026-11-01T13:41:56Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":20964,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-02T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":14275},"rest":"https://alion.io/mcp/rest/get_company?id=14275"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Ftd-synnex-physical-ai-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Ftd-synnex-physical-ai-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Ftd-synnex-physical-ai-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/td-synnex-physical-ai-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Ftd-synnex-physical-ai-engineer"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3368191}},{"tool":"search_jobs","arguments":{"company":"tdsynnex.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3368191},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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