Senior/Principal Forward Deployed Engineer - Applied AI/ML

Luminary Cloud; San Mateo (CA); On-site; USD 210,000 - 290,000; Full time

Job summary

Luminary Cloud in San Mateo, CA, is seeking a Senior/Principal Applied AI/ML Scientist to build Physics AI models that power customer outcomes. You will work at the boundary of cutting-edge research and applied delivery, designing architectures, training pipelines, and deploying models into production engineering workflows.

You will collaborate with Applications Engineers and Data & Platform teams, applying neural operators, diffusion models, and other physics-informed techniques while mentoring

Qualifications

• 5–10 years in applied ML with physics-informed work.
• Strong Python and PyTorch, production-ready code.
• Experience deploying models on engineering data.
• Familiarity with CFD/FEA or related simulations.
• Experience with distributed training and GPUs.
• Customer-facing communication ability.

Responsibilities

• Own model development for Physics AI engagements: architecture selection, training pipeline design, hyperparameter tuning, evaluation, and validation.
• Ensure training data is physically meaningful and adequate for the target use case.
• Partner with Data & Platform engineers to operationalize training pipelines, model registries, and inference serving.
• Collaborate with Luminary Research to apply state-of-the-art techniques to real customer problems.
• Embrace co-engineering: work with customer data scientists and engineers, sharing methodology.
• Bring back signal from delivery into Research and Product to guide next-gen Physics AI methods.
• Mentor junior team members and contribute to internal best practices for applied physics-informed ML.

Skills

Python

PyTorch

Production ML

Model deployment

Distributed training

GPU workloads

Physics-informed ML

Customer communication

Education

MS or PhD in CS/Applied Math/Physics/Engineering

Tools

PhysicsNeMo

JAX-based stacks

Job description

Full-time position | San Mateo, CA (Onsite)

JOIN THE REVOLUTION IN ENGINEERING INNOVATION

Luminary 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.

YOUR IMPACT

As 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.

WHAT YOU'LL DO

• Own model development for Physics AI engagements: architecture selection, training pipeline design, hyperparameter tuning, evaluation, and validation against ground‑truth simulation.
• Work with Applications Engineers to ensure training data is physically meaningful and adequate for the target use case.
• Partner with Data & Platform engineers to operationalize training pipelines, model registries, and inference serving.
• Collaborate with Luminary Research to apply state‑of‑the‑art techniques — neural operators, diffusion models, geometric deep learning, latent representations — to real customer problems.
• Embrace co‑engineering: work side‑by‑side with customer data scientists and engineers, sharing methodology and building model literacy on the customer side.
• Bring back signal from delivery into Research and Product, helping shape the next generation of Luminary's Physics AI methods and platform.
• Mentor junior team members and contribute to internal best practices for applied physics‑informed ML.

WHAT YOU BRING

• 5–10 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.
• Strong proficiency in Python and PyTorch (or equivalent deep learning framework). You write production‑quality ML code, not just research notebooks.
• Hands‑on experience training and deploying models on engineering or scientific data — surrogate models, neural operators, graph neural networks, diffusion models, or related architectures.
• Working 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.
• Experience with distributed training, GPU workloads, and modern ML infrastructure (experiment tracking, model registries, inference serving).
• Strong scientific mindset: rigorous experimentation, careful evaluation, honest reporting of what works and what does not.
• Customer‑facing presence; comfortable explaining model architectures and limitations to engineering audiences.
• Self‑starter mentality, persistent through iteration, willing to travel occasionally to customer sites.

PREFERRED QUALIFICATIONS

• Advanced degree (MS or PhD) in Computer Science, Applied Math, Physics, Engineering, or related quantitative discipline.
• Published work in physics‑informed ML, neural operators, scientific machine learning, or related fields.
• Experience with physics‑informed AI/ML frameworks (e.g. PhysicsNeMo, JAX‑based scientific ML stacks) or foundation model fine‑tuning pipelines for scientific data.
• Prior experience in applied research roles at engineering, simulation, or scientific computing companies.
• Track record of shipping models into production engineering workflows.