AI/ML Engineer

Role overview

Responsibilities

• check_circle Design and maintain LLM-based agentic systems for physics simulation workflows
• check_circle Build semantic search and retrieval pipelines over technical documentation and simulation data
• check_circle Develop embedding pipelines: chunking strategies, vector stores, retrieval evaluation
• check_circle Deploy and operate containerized ML services on AWS (ECS, Lambda, S3)
• check_circle Optimize LLM inference costs, latency, and quality at scale
• check_circle Integrate AI capabilities into IDE extensions (VS Code, Cursor) via MCP

Basic qualifications

• M.Sc. or Ph.D. in Computer Science, Machine Learning, or related field (or equivalent industry experience)
• 2+ years building production AI/ML systems (not just prototypes)
• Hands-on experience with LLM APIs (OpenAI, Anthropic) and prompt engineering
• Strong understanding of embeddings and vector databases (Weaviate, Chroma, pgvector)
• Proficiency in Python; working knowledge of TypeScript
• Track record of shipping AI features to end users

Preferred qualifications

• Experience with agentic LLM frameworks (LangChain, LlamaIndex, Pydantic AI, DSPy)
• Experience building LLM evaluation pipelines
• Familiarity with MCP (Model Context Protocol) or similar agent-tool interfaces
• Background in scientific/technical domains (physics, engineering, simulation)
• Production AWS experience (EC2, ECS, Lambda)
• Experience with containerization (Docker) and observability tooling
• Knowledge of traditional ML beyond LLMs

Benefits

• check_circle Competitive compensation with equity of a fast-growing startup.
• check_circle Medical, dental, and vision health insurance.
• check_circle 401(k) Contribution.
• check_circle Gym allowance.
• check_circle Friendly, thoughtful, and intelligent coworkers.