{"id":127965,"url":"https://alion.io/job/path-robotics-senior-machine-learning-engineer-reinforcement-learningworld-model","title":"Senior Machine Learning Engineer (Reinforcement Learning/World Model)","company":{"id":48166,"name":"Path Robotics","domain":"path-robotics.com","url":"https://alion.io/company/path-robotics","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":83,"open_postings":21,"ghost_share":0,"stale_share":0.667,"repost_share":0,"time_to_fill_p50_days":55,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Columbus, United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":142000,"max_usd":257000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":314},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Embodied AI","optional":false},{"name":"Isaac Gym","optional":false},{"name":"Machine Learning","optional":false},{"name":"MuJoCo","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Synthetic Data","optional":false},{"name":"TensorFlow","optional":false},{"name":"World Models","optional":false}],"status":"live","first_seen_at":"2026-07-13T21:37:16Z","employer_posted_date":"2026-08-05","last_verified_at":"2026-10-17T10:18:26Z","board_verified":true,"closed_at":null,"days_open":95,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":95},"description":"Build the Path Forward\nAt Path Robotics, we’re building the future of embodied intelligence. Our AI-driven systems enable robots to adapt, learn, and perform in the real world closing the skilled labor gap and transforming industries. We go beyond traditional methods, combining perception, reasoning, and control to deliver field-ready AI that is risk-aware, reliable, and continuously improving through real-world use.\nBig, hard problems are our everyday work, and our team of intelligent, humble, and driven people make the impossible possible together.\nManufacturing demands exceptionally high performance, reliability, and adaptability. Processes like welding involve fast, complex, and poorly modeled physics that traditional simulators struggle to capture - especially in the long tail of real-world conditions.\nWe are building intelligent robotic systems that learn directly from data by combining neural world models with reinforcement learning. Our goal is to give robots the ability to learn, predict, and plan in complex manufacturing environments by replacing or augmenting classical physics simulators with fast, high-fidelity learned ones.\nWe are seeking a Senior Machine Learning Engineer to lead the development of a neural welding simulator - a learned world model that captures the visual and physical dynamics of welding and enables large-scale RL training. This role sits at the intersection of generative modeling, robotics, and applied physics. It is research-heavy by design, while still grounded in production reality.\nWe are hiring two Senior Machine Learning Engineers with complementary specializations:\nSenior Machine Learning Engineer, World Models\nSenior Machine Learning Engineer, Reinforcement Learning\nSenior ML Engineer, World Models\nWhat You’ll Do\nBuild action-conditioned world models that predict how the welding process evolves under changes to robot motion and process parameters.\nModel relationships among inputs, system state, physical dynamics, and resulting weld quality.\nDevelop multimodal models using data such as video, 3D scans, thermal measurements, electrical signals, robot state, and process parameters.\nExplore latent dynamics, video prediction, generative modeling, and spatiotemporal representations.\nImprove long-horizon rollout accuracy, physical plausibility, temporal consistency, and computational efficiency.\nQuantify model uncertainty and identify conditions under which predictions are unreliable.\nValidate learned predictions against real-world welding data.\nIntegrate the model into RL, planning, process-optimization, evaluation, and synthetic-data workflows.\nPrevent downstream optimization systems from exploiting inaccuracies in the learned model.\nTranslate promising research into scalable training and inference systems.\nSenior ML Engineer, Reinforcement Learning\nWhat You’ll Do\nDevelop reinforcement learning approaches for optimizing welding decisions and process outcomes.\nDefine state, observation, action, and reward representations based on measurable manufacturing objectives.\nTrain and evaluate policies using learned world models, traditional simulation, offline datasets, and controlled real-world experiments.\nDevelop offline, model-based, or constrained RL methods suitable for limited and expensive physical interaction.\nOptimize across competing objectives such as weld quality, cycle time, reliability, energy use, and equipment constraints.\nDesign methods that account for uncertainty, distribution shift, delayed outcomes, and sparse or imperfect reward signals.\nDiagnose reward exploitation, unsafe behavior, policy instability, and model exploitation.\nEstablish reliable offline and real-world policy evaluation methods.\nPartner with controls, welding, robotics, world-model, data, and ML infrastructure engineers.\nTranslate research prototypes into dependable training, evaluation, and deployment systems.\nWho You Are\nMaster’s or PhD in Computer Science, Robotics, Machine Learning, or related field, or equivalent practical experience.\nExperience developing and deploying reinforcement learning algorithms on real-world systems.\nProficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.\nExperience with simulation environments (e.g., MuJoCo, Isaac Gym).\nSolid understanding of probability, statistics, and optimization.\nExperience with training and deploying ML models in production systems.\nWhy You'll Love It Here\nDaily free lunch to keep you fueled and connected with the team\nFlexible PTO so you can take the time you need, when you need it\nComprehensive medical, dental, and vision coverage\n6 weeks fully paid parental leave, plus an additional 6-8 weeks for birthing parents (12-14 weeks total)\n401(k) retirement plan through Empower\nGenerous employee referral bonuses-help us grow our team!\nWho We Are\nAt Path Robotics we love coming to work to solve interesting and tough challenges but also because our ideas are welcomed and valued. We encourage unique thinking and are dedicated to creating a diverse and inclusive environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.\nIf you require a reasonable accommodation to participate in the application process or any part of the hiring process, please contact . We are committed to providing equal access and will work with qualified individuals to ensure a fair and accessible hiring experience. We will respond to your request within 48 hours.","description_format":"text","description_chars":5625,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":["Parental leave","Retirement plans"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Industrial Robotics","Robotic Software & Control Systems"],"lifecycle":[{"event":"open","at":"2026-07-13T21:37:16Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 20","filings_12m":20,"filings_prev_12m":8,"green_card_filings_12m":0,"median_offered_wage_usd":167500,"route":null,"cap_exempt":false,"checked_at":"2026-10-15T11:24:14+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":8}],"liveness":{"score":22,"band":"cold","label":"Long shot","p_open":1,"p_active":0.48,"p_room":0.45,"age_days":95,"expected_fill_days":55,"reasons":["conf:2","stale_co","velocity","win:tail"],"computed_at":"2026-10-17T05:46:00Z"},"pay":null,"html_url":"https://alion.io/job/path-robotics-senior-machine-learning-engineer-reinforcement-learningworld-model","json_url":"https://alion.io/job/path-robotics-senior-machine-learning-engineer-reinforcement-learningworld-model.json","meta":{"generated_at":"2026-10-17T10:47:43Z","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":14114,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-18T00:00:00Z"}},"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":48166},"rest":"https://alion.io/mcp/rest/get_company?id=48166"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fpath-robotics-senior-machine-learning-engineer-reinforcement-learningworld-model"},{"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%2Fpath-robotics-senior-machine-learning-engineer-reinforcement-learningworld-model"},{"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%2Fpath-robotics-senior-machine-learning-engineer-reinforcement-learningworld-model"},{"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/path-robotics-senior-machine-learning-engineer-reinforcement-learningworld-model\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fpath-robotics-senior-machine-learning-engineer-reinforcement-learningworld-model"}]}