Machine Learning Engineering Manager, Public Sector

At Scale, our Public Sector Machine Learning team develops and deploys cutting‑edge AI systems into mission‑critical government environments. From advanced computer vision pipelines to agentic LLM frameworks, our work directly supports national security and defense partners. We are looking for a Machine Learning Engineering Manager to lead this team of world‑class ML engineers and help shape the future of AI in the public sector.

• Lead and grow a team of ML engineers delivering production‑ready AI systems for public sector customers.
• Provide technical direction and mentorship on projects spanning agentic LLM frameworks, reinforcement learning, generative AI, and computer vision.
• Collaborate with research, product, and infrastructure teams to align technical roadmaps with organizational and customer priorities.
• Drive operational excellence: establish best practices for model development, deployment, evaluation, and monitoring in secure, high‑stakes environments.
• Partner with public sector stakeholders to translate mission needs into scalable ML solutions.
• Work closely with public sector customers to scope and deliver AI applications.
• Ensure effective prioritization and resourcing across multiple programs and customer engagements.
• Cultivate a strong engineering culture that values collaboration, innovation, accountability, and impact.
• Support career development, performance reviews, and hiring to expand the team.

• US citizenship and US Government Security Clearance is a requirement (TS/SCI preferred)
• Proven experience managing and mentoring ML or AI engineering teams, ideally in applied research or production ML environments.
• Strong technical background in one or more of: computer vision, generative AI/LLMs, reinforcement learning, or agentic systems.
  • Up‑to‑date understanding of cutting edge ML research and production systems in your domain(s) of expertise.
• Hands‑on experience with deep learning frameworks (PyTorch, Tensor Flow) and large‑scale ML infrastructure.
• Background in deploying AI systems in high‑reliability or mission‑critical contexts (public sector, defense, healthcare, finance, etc.).
• Ability to communicate technical concepts effectively to both technical and non‑technical stakeholders, including government partners.
• Strong program management skills: ability to set strategy, manage multiple priorities, and deliver on commitments.

• Graduate degree in Computer Science, Machine Learning, or related field.
• Experience in public sector / defense AI programs.
• Familiarity with evaluation frameworks for LLMs and multi‑agent systems.
• Cloud platform (AWS/GCP/Azure) experience, especially in secure deployments.

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, determined by work location and additional factors, including job‑related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Directors 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.

Base salary range for this full‑time position in the locations of San Francisco, New York, Seattle is: $240,450 — $300,300 USD

Base salary range for this full‑time position in the locations of Washington DC, Texas, Colorado is: $216,300 — $269,850 USD

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…