{"id":3344519,"url":"https://alion.io/job/phyagents-machine-learning-engineer","title":"Machine Learning Engineer","company":{"id":4465011,"name":"PhyAgents","domain":"phyagents.com","url":"https://alion.io/company/phyagents","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":56000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":32},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"DeepSpeed","optional":false},{"name":"FSDP","optional":false},{"name":"Imitation Learning","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Vision-Language-Action","optional":false},{"name":"Sim-to-Real","optional":true}],"status":"live","first_seen_at":"2026-10-27T16:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-27T16:35:00Z","board_verified":false,"closed_at":null,"days_open":4,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":4},"description":"We are looking for an experienced Machine Learning Engineer to develop, train, evaluate, and deploy advanced ML models for real-world robotic systems. The role involves working across the complete machine learning lifecycle, including data engineering, model training, simulation, evaluation, and deployment on physical robots. The ideal candidate will have strong hands-on experience with PyTorch, distributed training, reinforcement learning, and production-grade ML systems, along with the ability to translate research into real-world robotic applications.\n\nResponsibilities: \nTrain and post-train robotic foundation models, including Vision-Language-Action (VLA) models, using imitation learning and reinforcement learning.\nDevelop and optimize complex PyTorch training pipelines, including multi-GPU and multi-node distributed training.\nBuild scalable training infrastructure, efficient data loaders, and reproducible experimentation pipelines.\nDevelop simulation environments and train robotic policies that transfer effectively to real-world hardware.\nDesign reward functions, task verifiers, and evaluation frameworks to measure model performance.\nBuild data pipelines to convert raw robotic episodes into versioned, training-ready datasets.\nDeploy and optimize ML models on physical robots, ensuring reliable performance and efficient inference.\nDebug and troubleshoot issues across models, datasets, simulation environments, and robotic hardware.\nOwn ML systems end-to-end, from problem definition and data preparation to training, evaluation, and deployment.\n\nRequirements: \n5+ years of hands-on experience training and deploying ML models in production or research environments.\nStrong expertise in Python and PyTorch, including writing and debugging complex training loops.\nExperience with distributed training frameworks such as PyTorch DDP, FSDP, DeepSpeed, or equivalent.\nExperience with multi-GPU or multi-node training and performance profiling.\nStrong understanding of machine learning fundamentals, linear algebra, probability, and optimization.\nKnowledge of reinforcement learning, imitation learning, and model evaluation.\nStrong data structures, algorithms, and systems fundamentals.\nExcellent software engineering practices and experience building maintainable, reliable ML systems.\nDemonstrated ownership of ML projects across the complete development and deployment lifecycle.\n\nPreferred Skills: \nExperience with robotics, robotic foundation models, or Vision-Language-Action (VLA) models.\nHands-on experience with simulation environments and sim-to-real transfer.\nExperience designing reward functions, task verifiers, and evaluation metrics for robotic tasks.\nExperience deploying and debugging ML models on real robotic hardware.\nSkills\nDDP, DeepSpeed, FSDP, Imitation Learning, PyTorch, Python, Reinforcement Learning","description_format":"text","description_chars":2848,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Manufacturing","Electronics 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