Machine Learning Engineer - Model Evaluations, Public Sector

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The Public Sector ML team at Scale deploys advanced AI systems—including LLMs, agentic models, and multimodal pipelines—into mission-critical government environments. We build evaluation frameworks that ensure these models operate reliably, safely, and effectively under real-world constraints. As an ML Engineer, you will design, implement, and scale automated evaluation pipelines that help customers trust and operationalize advanced AI systems across defense, intelligence, and federal missions.

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You will:

• Develop and maintain automated evaluation pipelines for ML models across functional, performance, robustness, and safety metrics, including LLM-judge–based evaluations.
  
  
• Design test datasets and benchmarks to measure generalization, bias, explainability, and failure modes.
  
  
• Build evaluation frameworks for LLM agents, including infrastructure for scenario-based and environment-based testing.
  
  
• Conduct comparative analyses of model architectures, training procedures, and evaluation outcomes.
  
  
• Implement tools for continuous monitoring, regression testing, and quality assurance for ML systems.
  
  
• Design and execute stress tests and red-teaming workflows to uncover vulnerabilities and edge cases.
  
  
• Collaborate with operations teams and subject matter experts to produce high-quality evaluation datasets.

• This role will require an active security clearance or the ability to obtain a security clearance.

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Ideally you’d have:

• Experience in computer vision, deep learning, reinforcement learning, or NLP in production settings.
  
  
• Strong programming skills in Python; experience with TensorFlow or PyTorch.
  
  
• Background in algorithms, data structures, and object-oriented programming.
  
  
• Experience with LLM pipelines, simulation environments, or automated evaluation systems.
  
  
• Ability to convert research insights into measurable evaluation criteria.

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Nice to haves:

• Graduate degree in CS, ML, or AI.
  
  
• Cloud experience (AWS, GCP) and model deployment experience.
  
  
• Experience with LLM evaluation, CV robustness, or RL validation.
  
  
• Knowledge of interpretability, adversarial robustness, or AI safety frameworks.
  
  
• Familiarity with ML evaluation frameworks and agentic model design.
  
  
• Experience in regulated, classified, or mission-critical ML domains.

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