{"id":69880,"url":"https://alion.io/job/luminary-seniorprincipal-forward-deployed-engineer-applied-aiml","title":"Senior/Principal Forward Deployed Engineer - Applied AI/ML","company":{"id":24801,"name":"Luminary","domain":"luminary.ai","url":"https://alion.io/company/luminary","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Rippling","truth_index":{"grade":"A","score":95,"open_postings":15,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":176,"computed_at":"2026-10-29T05:49:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Mateo, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":173000,"max_usd":359000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":782},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Diffusion Models","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Fine-tuning","optional":true},{"name":"JAX","optional":true}],"status":"live","first_seen_at":"2026-07-07T14:29:03Z","employer_posted_date":"2026-07-07","last_verified_at":"2026-10-29T06:24:23Z","board_verified":true,"closed_at":null,"days_open":113,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":113},"description":"Full-time position | San Mateo, CA (Onsite)\nJOIN THE REVOLUTION IN ENGINEERING INNOVATION\nLuminary helps engineering companies be more competitive by getting to market faster, creating better products, and reducing development risk. We do this through our Physics AI platform - the fastest and easiest way to build and deploy models that understand and instantly predict physical reality with precision. Our customers span industries from automotive and aerospace to defense, industrial, semiconductors, and energy - ranging from hyper-growth startups to Fortune 100 enterprises. Luminary is a Series B company headquartered in San Mateo, California.\nYOUR IMPACT\nAs a Senior/Principal Applied AI/ML Scientist on Luminary's Applied AI/ML team, you build the Physics AI models that power customer outcomes. You work in a matrix structure inside customer value delivery teams alongside a Lead Delivery Engineer, Applications Engineers, and Data & Platform engineers. You take real customer engineering problems, design and train the right model architectures, and partner with the team to deploy those models into production engineering workflows. You operate at the boundary of cutting-edge research and applied delivery - staying connected to the frontier of physics-informed ML while making sure your work ships and gets used.\nWHAT YOU'LL DO\nOwn model development for Physics AI engagements: architecture selection, training pipeline design, hyperparameter tuning, evaluation, and validation against ground-truth simulation.\nWork with Applications Engineers to ensure training data is physically meaningful and adequate for the target use case.\nPartner with Data & Platform engineers to operationalize training pipelines, model registries, and inference serving.\nCollaborate with Luminary Research to apply state-of-the-art techniques - neural operators, diffusion models, geometric deep learning, latent representations - to real customer problems.\nEmbrace co-engineering: work side-by-side with customer data scientists and engineers, sharing methodology and building model literacy on the customer side.\nBring back signal from delivery into Research and Product, helping shape the next generation of Luminary's Physics AI methods and platform.\nMentor junior team members and contribute to internal best practices for applied physics-informed ML.\nWHAT YOU BRING\n5-10 years of experience in applied machine learning, with significant exposure to scientific computing, engineering simulation, or physics-informed ML. Principal-level candidates trend toward the upper end of the range.\nStrong proficiency in Python and PyTorch (or equivalent deep learning framework). You write production-quality ML code, not just research notebooks.\nHands-on experience training and deploying models on engineering or scientific data - surrogate models, neural operators, graph neural networks, diffusion models, or related architectures.\nWorking knowledge of engineering simulation: CFD, FEA, EM, thermal, or related - enough to collaborate effectively with domain experts and understand what the model needs to learn.\nExperience with distributed training, GPU workloads, and modern ML infrastructure (experiment tracking, model registries, inference serving).\nStrong scientific mindset: rigorous experimentation, careful evaluation, honest reporting of what works and what does not.\nCustomer-facing presence; comfortable explaining model architectures and limitations to engineering audiences.\nSelf-starter mentality, persistent through iteration, willing to travel occasionally to customer sites.\nPREFERRED QUALIFICATIONS\nAdvanced degree (MS or PhD) in Computer Science, Applied Math, Physics, Engineering, or related quantitative discipline.\nPublished work in physics-informed ML, neural operators, scientific machine learning, or related fields.\nExperience with physics-informed AI/ML frameworks (e.g. PhysicsNeMo, JAX-based scientific ML stacks) or foundation model fine-tuning pipelines for scientific data.\nPrior experience in applied research roles at engineering, simulation, or scientific computing companies.\nTrack record of shipping models into production engineering workflows.","description_format":"text","description_chars":4175,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Machine Learning","Simulation & Digital Twin Software","AI for 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