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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Machine Learning Engineer, Public Sector - **Company:** Scale Inc - **Location:** Denver, CO, United States - **Experience:** Expert - **Salary:** $274,400.0 - $343,000.0 - **Contract:** Permanent contract - **Skills:** Geographic Information Systems, Artificial Intelligence, Systems Engineering, Automated Storage and Retrieval Systems, Customer Data Management, Decision Support Systems, Geospatial Intelligence, Python (Programming Language), Machine Learning, Regression Testing, Tensorflow, Spatial Data Infrastructures, Pytorch, Large Language Models, Multi-Agent Systems, Machine Learning Operations, Hardware Infrastructure - **Published:** September 11, 2026 - **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/18261758?backUrl=%2Fcareer%2F18261758%2FStaff-Machine-Learning-Engineer-Public-Sector-Colorado-Denver ## About the Role * 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 tradeoffs 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. ## Description $274,400spanspan class="divider"-spanspan$343,000 USDspandivdivdivdiv class="content-conclusion"pPLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role United States, Colorado, Denver Sep 10, 2026 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. 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