Staff Machine Learning Engineer, Public Sector
About the role
Lead design and deployment of agentic AI systems for government environments, building infrastructure for reasoning, planning, and action at national scale and partnering with defense/intelligence agencies on classified/regulatory deployments.
Responsibilities
- 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.
- Travel requirements: light travel (approximately 10%) for customer interaction and team needs.
Qualifications
- 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.
Skills
Benefits
- comprehensive health, dental and vision coverage
- retirement benefits
- learning and development stipend
- generous PTO
- commuter stipend (optional)
About Scale AI
Data-centric infrastructure to accelerate the development of AI
Scale accelerates the development of AI within organizations of any size to deliver critical business insights and operational efficiency. Its data-centric infrastructure platform leverages RLHF (Reinforced Learning with Human Feedback) to help organizations build the strongest AI models that supercharge their business, with customers across industries including Meta, Microsoft, U.S. Army, DoD’s Defense Innovation Unit, Open AI, General Motors, Toyota Research Institute, Brex, Instacart and Flexport.
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