LLM Engineer

Luminary Cloud 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.

Luminary Cloud 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.

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.

• 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

• 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

• 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