Senior Machine Learning Engineer - Model Evaluations, Public Sector

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, 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. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is: $240,450 — $300,300 USD Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of Washington DC, Texas, Colorado, Hawaii is: $216,300 — $269,850 USD 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, Ernst & Young, 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 [email protected]. 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 . PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our privacy policy for additional information.

{
  "content_hash": "0607f6c8b0de563b66d0df7fb59f7c0413260529c7674a48f19a0445ad552a58",
  "source_hash": "f3a9615ce55c983e72292f71270e0de71acaa18cadfe67975031dcee9cf7df8b",
  "last_changed_at": "2026-06-04T11:10:19.079Z",
  "active_status": "active"
}

{
  "language": "en",
  "location": {
    "raw": "San Francisco, CA",
    "city": "San Francisco",
    "region": "CA",
    "country": "United States",
    "is_remote": false,
    "confidence": 0.9
  },
  "salary_max": 300300,
  "salary_min": 240450,
  "inferred_at": "2026-06-06T20:10:24.285Z",
  "launch_scope": {
    "reason": "english_us_canada",
    "included": true,
    "language": "en",
    "location": {
      "raw": "San Francisco, CA",
      "city": "San Francisco",
      "region": "CA",
      "country": "United States",
      "is_remote": false,
      "confidence": 0.9
    },
    "countries": [
      "United States"
    ]
  },
  "remote_policy": null,
  "salary_period": "year",
  "workplace_type": null,
  "salary_currency": "USD"
}

{}

{
  "title": "Senior Machine Learning Engineer - Model Evaluations, Public Sector",
  "offices": [
    {
      "id": 4001481005,
      "name": "Washington, DC",
      "location": "Washington, District of Columbia, United States",
      "child_ids": [],
      "parent_id": 4019441005
    }
  ],
  "language": "en",
  "location": {
    "name": "San Francisco, CA; St. Louis, MO; New York, NY; Washington, DC"
  },
  "metadata": [
    {
      "id": 7904378005,
      "name": "Domain",
      "value": null,
      "value_type": "multi_select"
    },
    {
      "id": 9832886005,
      "name": "External Department",
      "value": "Engineering",
      "value_type": "single_select"
    }
  ],
  "updated_at": "2026-06-03T15:21:47-04:00",
  "departments": [
    {
      "id": 4136578005,
      "name": "Public Sector Engineering",
      "child_ids": [],
      "parent_id": 4003434005
    }
  ],
  "company_name": "Scale AI",
  "requisition_id": 4214146005,
  "first_published": "2025-11-18T17:39:10-05:00",
  "application_deadline": null
}