Staff Machine Learning Engineer, Public Sector

Scale AI, Inc.; Washington (District of Columbia); On-site; USD 274,000 - 343,000; Full time

Benefits offered by this job

Health, dental and vision coverage

Retirement benefits

Learning and development stipend

Generous PTO

Commuter stipend

Job summary

Scale AI, Inc. seeks a Staff Machine Learning Engineer for the Public Sector to design and deploy agentic AI systems in government environments in Washington, DC.

You will lead architectures, build agents for geospatial tasks, and surface high‑signal context in large document collections. You will also tune embeddings, design memory systems, and mentor teams while partnering with managers and researchers to drive production‑ready capabilities.

Qualifications

• 8+ years of experience building and deploying applied ML systems.
• Deep experience with agentic systems, autonomous workflows, or multi-step reasoning.
• Strong background in ML systems engineering (model serving, pipelines, monitoring, evaluation).
• Hands-on experience with retrieval systems, embeddings, or representation learning.
• Proficiency in Python and modern ML frameworks (e.g., PyTorch), with end-to-end system design.
• Capability to operate at Staff‑level scope: set direction, own ambiguous problems, drive initiatives to production.
• Experience making trade-offs across performance, cost, reliability and development velocity.

Responsibilities

• Lead the architecture and implementation of agentic AI systems, with a focus on long‑horizon reasoning, orchestration, and system‑level reliability.
• Build and scale agents that perform complex geospatial reasoning, including interpreting, generating, and reasoning over maps and spatial data.
• Design and improve retrieval systems across large collections of documents to surface context efficiently.
• Fine‑tune and evaluate embedding models to improve recall and precision for mission‑critical datasets.
• Design memory systems that allow agents to persist state over long contexts and learn from interactions.
• Own and evolve shared agentic infrastructure and core libraries for reuse across teams and contracts.
• Define evaluation strategies for robustness and regression testing in production.
• Partner with engineering managers, product leaders, and researchers to scope initiatives and unblock execution.
• Serve as a technical mentor, raising the bar for system design and production readiness.
• Comfortable with light travel (~10%) for customer interaction.

Skills

Agentic ML

ML systems eng

Python

PyTorch

Staff-level leadership

Retrieval systems

Geospatial reasoning

System design

Trade-offs

Tools

PyTorch

Job description

The goal of a Staff Machine Learning Engineer at Scale is to lead the design and deployment of agentic AI systems that operate in real-world, mission‑critical government environments. On the Public Sector team, you'll work at the intersection of agentic ML, systems engineering, and applied research, building foundational infrastructure that enables AI systems to reason, plan, and act reliably at national scale.

Our Public Sector ML Team partners directly with U.S. defense and intelligence agencies to deploy AI into classified and regulated environments. Through flagship programs like Donovan and Thunderforge, we are advancing the next generation of agentic AI for geospatial reasoning, planning, and decision support. Staff Machine Learning Engineers play a central role in setting technical direction, owning core architectures, and translating ambitious ideas into production systems trusted by government operators.

You will:

• Lead the architecture and implementation of agentic AI systems, with a focus on long‑horizon reasoning, orchestration, and system‑level reliability.
• Build and scale agents that perform complex geospatial reasoning, including interpreting, generating, and reasoning over maps and spatial data.
• Design and improve retrieval systems across large collections of static and semi‑structured documents, enabling agents to surface high‑signal context efficiently.
• Fine‑tune and evaluate embedding models to improve recall and precision for mission‑critical datasets.
• Design memory systems that allow agents to persist state, operate over long contexts, and learn from prior interactions.
• Own and evolve shared agentic infrastructure and core libraries, enabling reuse across teams, products, and Public Sector contracts.
• Define evaluation strategies for agentic systems, including robustness testing, failure‑mode analysis, and regression testing in production environments.
• Partner closely with engineering managers, product leaders, and researchers to scope high‑impact initiatives and unblock execution across teams.
• Serve as a technical mentor and multiplier‑raising the bar for system design, ML rigor, and production readiness across the organization.
• Comfortable with light travel (approximately 10%) for customer interaction and team needs.

This role will require an active TS security clearance.

Ideally You'd Have:

• 8+ years of experience building and deploying applied ML systems in production environments.
• Deep experience with agentic systems, autonomous workflows, or ML systems that reason and act over multiple steps.
• Strong background in ML systems engineering, including model serving, pipelines, monitoring, and evaluation.
• Hands‑on experience with retrieval systems, embeddings, or representation learning.
• Proficiency in Python and modern ML frameworks (ex: PyTorch), with the ability to design systems end‑to‑end.
• Demonstrated ability to operate at Staff‑level scope: setting technical direction, owning ambiguous problems, and driving 01 initiatives to production.
• Experience making thoughtful trade‑offs across performance, cost, reliability, and development velocity.

Nice to Haves:

• Experience deploying ML systems into air‑gapped, classified, or otherwise disconnected environments – customer data centers, on‑prem infrastructure, or networks with no path to a cloud provider.
• Prior work with DoD, the intelligence community, or federal mission users – including the judgment to learn a mission well enough to know what "correct" means for the operator using your system.
• Hands‑on experience with geospatial data or GEOINT: reasoning over maps, imagery, or spatial reference systems.
• Depth in model adaptation – raining or fine‑tuning embedding models, instruction tuning, LoRA/PEFT, or RLHF.
• Experience building evaluation infrastructure for non‑deterministic systems: LLM‑as‑judge, regression suites for agent behavior, or drift detection in production.
• A track record of turning a forward‑deployed prototype into a supported, documented capability other engineers can deploy without you.

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.

The base salary range for this full‑time position in the location of Washington DC is $274,400 — $343,000 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 [e-mail]. 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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