Senior/Principal Forward Deployed Engineer - Applied AI/ML

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.