Senior Machine Learning Engineer - Model Evaluations, Public Sector

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

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Salary

$216K-$300K From the posting source

401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

131% above the BLS role benchmark for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source

Seniority

Senior From the posting source

Schedule

Shift type

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Weekend work

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Company

Equity

Offered From the posting source

Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Senior Machine Learning Engineer - Model Evaluations, Public Sector San Francisco, CA; St. Louis, MO; New York, NY; Washington, DC Senior 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. Comfortable with light travel (approximately 10%) for customer interaction and team needs. 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,

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