Staff Software Engineer – AI Platform
United States of America, Eagan, MinnesotaAll locationsUnited States of America, Eagan, MinnesotaUnited States of America, Frisco, TexasUnited States of America, Brooklyn, New YorkCanada, Toronto, OntarioUnited States of America, Ann Arbor, Michigan Hybrid
$158,000 a yearJobFig found this opening at its original source and checks that it remains available.
About the role
As a Lead Software Engineer (Staff Engineer), you will own core backend and AI orchestration systems that turn frontier models into reliable, production‑grade workflows at scale. This role is for the CoCounsel for Audit team, formed from the recent acquisition of the startup Materia . You will be a Lead Software Engineer (Staff Engineer) responsible for the backend and orchestration layer powering AI agents, conversations, workspaces, and knowledge base across CoCounsel for Audit.
What you'll bring
- You are a fit for the position of Lead Software Engineer (Staff Engineer) if your background includes:
- Bachelor’s degree in computer science, Computer Engineering, a related field, or equivalent experience
- 5-7+ years of experience in full-stack development, building scalable cloud-based applications, web services, APIs and AI-driven products.
- Deep Python expertise and experience with production systems using frameworks like FastAPI (or similar), relational databases (PostgreSQL or equivalent), and a major cloud provider.
- Strong background in distributed systems: data modeling, API contracts, observability, resilience patterns, and performance tuning under load.
- Proven track record leading large, complex projects end‑to‑end: architecture, execution, rollout, and long‑term operation.
- Excellent communication skills and the ability to partner with product, design, and ML teams in a fast‑moving environment.
- Demonstrated interest in AI systems and new engineering paradigms: LLMs, agents, retrieval, or similar – you care about what “AI‑native” software should look like.
- Hands‑on experience integrating LLM APIs (e.g., OpenAI, Anthropic) into production applications, including prompt/response management, cost controls, and safety considerations.
- Experience with AI‑adjacent infrastructure: vector databases, embeddings, semantic search, or custom retrieval pipelines.
- Opinions and experience around automated testing, reliability, and release practices for systems with non-deterministic model behavior.
- Prior experience in domains where correctness, auditability, and compliance matter (fintech, tax, audit, legal, or similar), or strong interest in applying AI in those contexts.