The opportunity
The goal of a Staff Machine Learning Engineer at Scale is to lead the design and deployment of agentic AI systems that operate in real-world, mission-critical government environments. On the Public Sector team, you’ll work at the intersection of agentic ML, systems engineering,…
What you'll do
Lead the architecture and implementation of agentic AI systems, with a focus: on long-horizon reasoning, orchestration, and system-level reliability.
Build and scale agents that perform complex geospatial reasoning, including: interpreting, generating, and reasoning over maps and spatial data.
Design and improve retrieval systems across large collections of static and: semi-structured documents, enabling agents to surface high-signal context efficiently.
Fine-tune and evaluate embedding models to improve recall and precision for mission-critical datasets.
Design memory systems that allow agents to persist state, operate over long: contexts, and learn from prior interactions.
Own and evolve shared agentic infrastructure and core libraries, enabling: reuse across teams, products, and Public Sector contracts.
What they're looking for
- Define evaluation strategies for agentic systems, including robustness: testing, failure-mode analysis, and regression testing in production environments.
- Partner closely with engineering managers, product leaders, and researchers: to scope high-impact initiatives and unblock execution across teams.
- Serve as a technical mentor and multiplier—raising the bar for system design,: ML rigor, and production readiness across the organization.
- Comfortable with light travel (approximately 10%) for customer interaction and team needs.
