LLM Engineer

ABOUT LUMINARY

LUMINARY HELPS ENGINEERING COMPANIES BE MORE COMPETITIVE BY GETTING TO MARKET

FASTER, CREATING NEW, BETTER PRODUCTS, AND REDUCING DEVELOPMENT RISK. WE DO THIS

WITH OUR PHYSICS AI PLATFORM, THE FASTEST AND EASIEST WAY TO BUILD AND DEPLOY

MODELS TO UNDERSTAND AND INSTANTLY PREDICT PHYSICAL REALITY WITH PRECISION.

CUSTOMERS SPAN INDUSTRIES FROM AUTOMOTIVE AND AEROSPACE, TO LEADING SPORTING

EQUIPMENT PROVIDERS, INCLUDING OTTO AVIATION, JOBY AVIATION, PIPER AIRCRAFT AND

TREK BIKES. LUMINARY IS A SERIES B COMPANY AND IS HEADQUARTERED IN SAN MATEO,

CALIFORNIA.

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THE ROLE

We're looking for an LLM Engineer to architect our Physics AI Copilot—the next

generation of intelligent assistants for engineering workflows. You'll work at

the intersection of large language models and domain-specific engineering

challenges, creating AI experiences that dramatically accelerate how engineers

work.

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RESPONSIBILITIES

* Develop Agentic AI systems: Design and implement tools for agents to call;

build reasoning, planning, and orchestration capabilities that enable the

copilot to autonomously execute complex engineering workflows

* Design and optimize RAG pipelines: Build retrieval-augmented generation

systems over engineering documentation, physics simulation results, and

domain knowledge bases

* Implement memory and context management: Create persistent conversation

memory and context systems that maintain coherent, long-running engineering

sessions

* Fine-tune and adapt LLMs: Customize foundation models for Physics AI and

physics simulation domain expertise through fine-tuning, prompt engineering,

and evaluation frameworks

* Deploy and scale LLM infrastructure: Build robust, production-grade systems

for self-hosting and serving LLMs, optimizing for latency, cost, and

reliability

* Integrate with Physics AI and physics simulation platform: Connect LLM

capabilities with Luminary's Physics AI training/evaluation/inference

pipelines, physics simulation solvers, mesh tools, and analytics APIs to

enable end-to-end automation

* Establish evaluation frameworks: Define metrics and build testing

infrastructure to measure copilot quality, accuracy, and user satisfaction

* Collaborate cross-functionally: Work closely with Physics AI researchers,

platform engineers, and product teams to deliver customer-centric AI

experiences

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QUALIFICATIONS

REQUIRED

* Bachelor's degree or higher in Computer Science, Mechanical Engineering,

Aerospace Engineering, or related field

* 5+ years of experience building production software or ML systems

* 2+ years of hands-on experience developing LLM-powered applications

* Strong proficiency in Python

* Proficiency using coding agents such as Claude Code

* Experience with Agent Evals

* Deep understanding of LLM architectures, prompting techniques, and their

capabilities/limitations

* Experience designing tools/functions for agents to call, with planning and

reasoning

* Experience with multi-agent orchestration and coordination

* Hands-on experience with RAG systems and memory/context management, including

vector databases, embedding models, chunking strategies, and long-running

session handling

* Experience building MCP (Model Context Protocol) servers to expose tools and

capabilities to external agents

* Experience with agent frameworks (e.g., LangChain, LlamaIndex, Google ADK,

Autogen, Claude Agent SDK, or custom solutions)

* Familiarity with Physics AI, CAE, or physics simulation domains a plus

* Experience fine-tuning LLMs for domain-specific applications

* Hands-on experience self-hosting and serving LLMs in production environments

NICE TO HAVE

* Experience with TypeScript for full-stack development

* Experience with Go for backend systems

* Familiarity with Kubernetes for container orchestration and deployment

* Experience with GPU infrastructure and optimization for LLM inference

* Experience deploying ML systems on cloud platforms (GCP, AWS, Azure) or

on-prem infrastructure

* Background in CFD, structural analysis, or thermal simulation

* Experience building developer tools or copilot-style products

* Contributions to open-source LLM projects or research publications