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# Staff Software Engineer, AI

- **Company:** PEOPLE INC.
- **Location:** Millbrae, CA, United States
- **Salary:** $280,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Systems Engineering, Distributed Systems, Python (Programming Language), PostgreSQL, TypeScript, Management of Software Versions, Large Language Models, Backend
- **Published:** September 12, 2026
- **Apply:** https://www.disabledperson.com/jobs/75110319-staff-software-engineer-ai

## About the Role

* Ample experience in backend or systems engineering
 * Exposure to applied AI systems (LLMs, NLP, RAG)
 * Proficiency in Python; experience with distributed systems is a plus
 * Familiarity with embeddings, vector DBs, and prompt iteration
 * Ability to move fast and own projects from idea through production
 * Bonus: experience building eval frameworks or internal ML infra

Tech Stack

 * Python, TypeScript
 * Postgres, Temporal
 * LLM APIs (e.g., OpenAI, Claude), vector databases
 * Prompting libraries, orchestration tooling

## Description

This company is using automation and applied AI to modernize an industry riddled with manual workflows and inefficiencies. By combining domain-specific expertise with cutting-edge LLMs and RAG pipelines, they're building intelligent tools that make expert work faster, smarter, and more consistent.

The Role

You'll be part of a cross-functional AI task force reporting into engineering leadership, working closely with product, design, and infra. This is a hands-on, end-to-end role ideal for engineers who want to prototype, ship, and scale real features - not just build demos. You'll split your time between user-facing copilots and the backend infra that powers them.

What You'll Do

 * Build LLM-powered assistants to streamline domain expert workflows
 * Design RAG pipelines using structured and unstructured internal data
 * Develop internal ML tooling for versioning, observability, and orchestration
 * Tune prompts, build eval harnesses, and iterate based on real-world feedback
 * Contribute to platform components that enable scalable experimentation