Staff Machine Learning Engineer

Responsibilities

• Lead the architecture and implementation of agentic AI systems for long-horizon reasoning, orchestration, and system-level reliability.
• Build agents for geospatial reasoning over maps and spatial data.
• Design retrieval systems for large collections of static and semi-structured documents.
• Fine-tune and evaluate embedding models for mission-critical datasets.
• Design memory systems for persistent state, long contexts, and learning from prior interactions.
• Own shared agentic infrastructure and core libraries across teams, products, and Public Sector contracts.
• Define evaluation strategies including robustness testing, failure-mode analysis, and production regression testing.
• Partner with engineering managers, product leaders, and researchers to scope initiatives and unblock execution.
• Mentor engineers and raise standards for system design, ML rigor, and production readiness.
• Travel approximately 10% for customer interactions and team needs.

Requirements

• 8+ years of experience building and deploying applied ML systems in production environments.
• Deep experience with agentic systems, autonomous workflows, or multi-step reasoning and acting ML systems.
• Strong ML systems engineering background, including model serving, pipelines, monitoring, and evaluation.
• Hands-on experience with retrieval systems, embeddings, or representation learning.
• Proficiency in Python and modern ML frameworks such as PyTorch, with end-to-end system design ability.
• Staff-level experience setting technical direction, owning ambiguous problems, and driving initiatives from 0 to 1 into production.
• Experience balancing performance, cost, reliability, and development velocity.
• Active TS security clearance.
• Preferred experience with air-gapped, classified, disconnected, on-premises, or customer data-center deployments.
• Preferred experience with DoD, intelligence community, or federal mission users.
• Preferred hands-on experience with geospatial data or GEOINT.
• Preferred experience with embedding-model training or fine-tuning, instruction tuning, LoRA/PEFT, or RLHF.
• Preferred experience building evaluation infrastructure for non-deterministic systems, including LLM-as-judge, agent regression suites, or production drift detection.
• Preferred experience turning forward-deployed prototypes into supported and documented capabilities.

Benefits

• Base salary, equity eligibility, and comprehensive health, dental, and vision coverage are provided for eligible roles.
• Retirement benefits, a learning and development stipend, and generous paid time off are offered.
• The role may be eligible for a commuter stipend.
• The position is full-time in Washington, DC, with approximately 10% travel.
• Candidates must wait 90 days before reconsideration for the same role.