{"id":9711,"url":"https://alion.io/job/skild-machine-learning-engineer-reinforcement-learning","title":"Machine Learning Engineer, Reinforcement Learning","company":{"id":3377,"name":"Skild AI","domain":"skild.ai","url":"https://alion.io/company/skild","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":74,"open_postings":48,"ghost_share":0.354,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":161,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pittsburgh, United States","San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":100000,"max":300000,"currency":"USD","period":"year","gross":null,"usd_annual":300000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"AI Agents","optional":true},{"name":"C++","optional":true},{"name":"JAX","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"PyTorch C++","optional":true},{"name":"TensorFlow","optional":true},{"name":"TensorFlow C++","optional":true}],"status":"live","first_seen_at":"2024-09-11T22:32:53Z","employer_posted_date":"2026-04-15","last_verified_at":"2026-10-17T10:46:51Z","board_verified":true,"closed_at":null,"days_open":765,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":765},"description":"Company Overview\nAt Skild AI, we are building the world's first general purpose robotic intelligence that is robust and adapts to unseen scenarios without failing. We believe massive scale through data-driven machine learning is the key to unlocking these capabilities for the widespread deployment of robots within society. Our team consists of individuals with varying levels of experience and backgrounds, from new graduates to domain experts. Relevant industry experience is important, but ultimately less so than your demonstrated abilities and attitude. We are looking for passionate individuals who are eager to explore uncharted waters and contribute to our innovative projects.\nPosition Overview\nWe are looking for a Machine Learning Engineer to be responsible for designing and implementing cutting-edge reinforcement learning algorithms, conducting experiments, and optimizing these models to perform efficiently in real-world robotic environments. This will require close collaboration with our robotics, research, and engineering team. Your work will directly impact the development of intelligent, adaptable robots capable of learning and performing complex tasks autonomously.\nResponsibilities\nDevelop and implement state-of-the-art reinforcement learning algorithms for robotic applications.\nDesign and conduct experiments to train RL models and conduct real-world tests.\nCollaborate closely with researchers to explore novel methods of scaling up reinforcement learning model training.\nCommunicate effectively with inference, application, and deployment engineers to integrate RL models into robotic systems and iterate on methods to enable robust deployment.\nAnalyze and interpret experimental results, iterating on model design to achieve desired performance.\nStay up-to-date with the latest research and advancements in reinforcement learning.\nPreferred Qualifications\nBS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.\nProficiency in Python, C++, or similar and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc.\nDeep understanding and practical experience with various reinforcement learning algorithms and techniques (model-free, model-based, multi-task, hierarchical, multi-agent, etc.).\nStrong background in algorithms, data structures, and software engineering principles.\nExperience with physics simulation engines and tools for training RL.\nDeep understanding of state-of-the-art machine learning techniques and models.\nExtensive industry experience with reinforcement learning and robotic systems.\nBase Salary Range\n$100,000—$300,000 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