The role
- COMP
- $175K - $230K
- EQUITY
- Competitive equity
- LOCATION
- Washington DC
- WORKPLACE
- Hybrid
- EXPERIENCE
- 2 - 10 years
- VISA
- None
- STACK
- Python
- INDUSTRY
- AI, Government, Defense, Security
/roles — ROLE_335
Member of Technical Staff
Early-stage AI team helping federal agencies catch fraudulent contractors and wasteful spending
The company
Small AI company building agentic systems that help U.S. government agencies uncover fraudulent, wasteful, and abusive spending.
- STAGE
- seed-stage
- FUNDING
- seed funding
- TEAM
- founding team of <10
- FOUNDED
- 2025
JD — the work
About the role
In this forward deployed ML role, you would build agentic AI and deliver it to U.S. government partners fighting fraud and waste, a problem measured in the trillions of dollars. You would work on site with federal agencies to understand their mission needs, then turn those into systems that work in practice. The team is very small and based in Washington, D.C., with a hybrid setup and local travel to government sites.
What you'll do
- Build and deploy tool-calling agents, retrieval-augmented pipelines, and multi-agent systems for high-stakes government missions
- Work side by side with agency teams to understand what their missions require
- Mine open and access-controlled government data to flag fraudulent contractors before more money is lost
- Visit the Pentagon and other federal sites within the D.C. area, with no out-of-state travel
What they're looking for
- Around 4 to 6 years of ML engineering experience
- Hands-on experience building agentic systems, RAG pipelines, or multi-agent frameworks in Python
- Comfort embedding with government stakeholders and turning mission needs into working software
- Ability to work hybrid in Washington, D.C., with local travel to agency sites