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Salary
$115k – $252k per year (Estimated)
Location
In office (San Mateo)
Seniority
Senior · 5+ years exp
Employment
Full-Time
Overview
Company
Impact
Profile match
Luminary (Luminary Cloud, Inc.) is a computer-aided engineering and enterprise AI platform headquartered in San Mateo, California. Founded in 2022 by Jason Spreti and Srinath Srinivasan, the company develops a "Physics AI" simulation platform that integrates large physics models (LPMs) with high-performance computing (HPC) cloud infrastructure. Luminary enables aerospace, automotive, defense, and industrial engineering teams to train AI models on fluid dynamics, structural mechanics, thermal fields, and electromagnetics.

Full Time | Eligible for Hybrid, Remote

JOIN THE REVOLUTION IN ENGINEERING INNOVATION

Engineering simulation has never been faster or more capable - and yet the hardest design challenges still outpace what traditional solvers can deliver at speed. Physics AI doesn't replace that foundation. It extends it, giving engineering teams a new capability layer that opens up problems previously constrained by compute time, iteration cost, or workflow bottlenecks. Luminary AI is building that capability layer, and our platform is deployed at several of the most recognized names in aerospace, automotive, and defense industries. We're Series B and growing fast.

ABOUT THE ROLE

As a Senior Forward Deployed Engineer on Luminary's Applications Engineering team, you're the physics-domain expert inside a customer value delivery team. You work in a matrix under a Lead Delivery Engineer, alongside Applied AI/ML Scientists, Data & Platform engineers, and product engineers. You take the hardest simulation problems a customer brings and turn them into deployed Physics AI solutions - from data generation, through model development, into production workflows - embedded alongside customer engineers.

WHAT YOU'LL DO

  • Lead the technical delivery of Physics AI engagements as the resident physics expert on the value delivery team.
  • Translate customer engineering problems into simulation campaigns, training datasets, and surrogate model architectures.
  • Run the simulations that generate high-quality training data, validating physical fidelity with customer SMEs.
  • Partner with Applied AI/ML Scientists on model selection, training, and validation against ground-truth simulations.
  • Work with Data & Platform engineers to integrate models into production workflows.
  • Build trust with customer engineers through technical credibility and shipped outcomes.
  • Travel to customer sites to support deployment, training, and engagement reviews.
  • Bring customer signals back to Product and Research to shape the Physics AI roadmap.

WHAT YOU BRING

  • Industrial electromagnetics experience, with hands-on depth in a core discipline.
  • Proficiency in Python for scripting and automation, plus familiarity with a deep learning framework (e.g., PyTorch) and API/SDK workflows.
  • Hands-on experience applying Physics AI or ML-based surrogate models to engineering simulation, including surrogate modeling, neural operators, or data-driven solvers.
  • Pre-sales or customer-facing delivery experience, including running technical evaluations, building POCs, and scoping engagements.
  • Strong communication with technical audiences.

WHO WE'RE LOOKING FOR

  • A PhD in Electrical Engineering, Physics, or a related discipline (MS considered with 5+ years of relevant industry experience), with hands-on depth in high-frequency applications (e.g., antenna design, EMC/EMI, signal integrity, radar cross-section, or RF/microwave engineering) and/or low-frequency applications (e.g., electric machine and motor design, induction heating, or wireless power transfer). Fluency in commercial solvers - Ansys HFSS / Maxwell, CST Studio Suite, COMSOL Multiphysics, FEKO, or similar - and the underlying numerical methods (full-wave FEM, MoM, or FDTD) is expected. Working knowledge of Physics AI and ML surrogates is preferred.

Eligibility: Limited to U.S. Persons; not eligible for visa sponsorship now or in the future.

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