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, and applied research, building foundational infrastructure that enables AI systems to reason, plan, and act reliably at national scale.

Our Public Sector ML Team partners directly with U.S. defense and intelligence agencies to deploy AI into classified and regulated environments. Through flagship programs like Donovan and Thunderforge, we are advancing the next generation of agentic AI for geospatial reasoning, planning, and decision support. Staff Machine Learning Engineers play a central role in setting technical direction, owning core architectures, and translating ambitious ideas into production systems trusted by government operators.

You will:

• 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.
• 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.

This role will require an active TS security clearance. 

Ideally You’d Have: 

• 8+ years of experience building and deploying applied ML systems in production environments.
• Deep experience with agentic systems, autonomous workflows, or ML systems that reason and act over multiple steps.
• Strong background in ML systems engineering, including model serving, pipelines, monitoring, and evaluation.
• Hands-on experience with retrieval systems, embeddings, or representation learning.
• Proficiency in Python and modern ML frameworks (ex: PyTorch), with the ability to design systems end to end.
• Demonstrated ability to operate at Staff-level scope: setting technical direction, owning ambiguous problems, and driving 0→1 initiatives to production.
• Experience making thoughtful tradeoffs across performance, cost, reliability, and development velocity.

Nice to Haves: 

• Experience deploying ML systems into air-gapped, classified, or otherwise disconnected environments - customer data centers, on-prem infrastructure, or networks with no path to a cloud provider.
• Prior work with DoD, the intelligence community, or federal mission users - including the judgment to learn a mission well enough to know what "correct" means for the operator using your system.
• Hands-on experience with geospatial data or GEOINT: reasoning over maps, imagery, or spatial reference systems.
• Depth in model adaptation - training or fine-tuning embedding models, instruction tuning, LoRA/PEFT, or RLHF.
• Experience building evaluation infrastructure for non-deterministic systems: LLM-as-judge, regression suites for agent behavior, or drift detection in production.
• A track record of turning a forward-deployed prototype into a supported, documented capability other engineers can deploy without you.