Machine Learning Engineer - Model Evaluations, Public Sector

Machine Learning Engineer - Model Evaluations, Public Sector

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.

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.

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.

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.

PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.

About Us:

At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.

We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status. 

We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information.

We comply with the United States Department of Labor's Pay Transparency provision. 

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