Job description

About Scale Scale’s mission is to develop reliable AI systems for the world’s most important decisions. As the leading AI data foundry, we provide the high-quality data and full-stack technologies that power the world’s most advanced models — fueling breakthroughs in generative AI, defense, and autonomous vehicles. We partner with leading enterprises and governments to bring AI into production that performs when it matters most, combining rigorous evaluation with full-stack deployment so our customers can build AI they can trust. About the Team Applied Intelligence Systems (AIS) is part of the Scale Generative AI Platform (SGP), focused on pushing the frontier of what agentic applications can do across diverse enterprise and government use cases. We build the infrastructure and tooling that power agentic AI in production, paired with applied ML research, design, and evaluation to ensure these systems perform reliably at the scale our customers demand. AIS spans multiple workstreams — agent evaluation and oversight, orchestration and tool-use infrastructure, model and systems optimization, and applied research on new agent capabilities — and this role is not scoped to any single one of them. We’re growing fast, with increasing traction across both commercial and public sector customers, and we’re just getting started — this team will define what dependable, production-grade agentic AI looks like. About the Role As a Staff Machine Learning Research Engineer, you will operate across the full breadth of AIS’s technical needs — wherever the hardest ML problem in agentic AI happens to be that quarter. This could mean training and fine-tuning models, designing evaluation and observability systems, building improvement loops from production data, prototyping novel agent architectures, or designing internal systems and tooling that boost productivity across teams. You’re not tied to one team’s roadmap; you’re expected to move to where the technical leverage is highest, and to set the AI/ML technical direction across AIS — the methods, architectures, and standards other teams build on, not just your own workstream. This is a hands-on research and engineering role at staff scope: you’ll write code — training pipelines, evaluation systems, infrastructure, or whatever the problem calls for — and ship production systems yourself, while also setting AIML technical direction and raising the bar for engineers and scientists across AIS. You will: Move across AIS’s core problem areas as needed — training/fine-tuning, inference, memory and retrieval, evaluation and observability, orchestration and tool-use infrastructure, applied research on new agent capabilities — going wherever the technical leverage is highest rather than owning one fixed surface Research and prototype novel methods for agent performance improvement in a production/enterprise-ready setting — continuous learning loops, automated curriculum or data generation from production traces, online or offline RL — and validate them with rigorous experiments before they ship, making the call on where to build new infrastructure versus apply existing methods Build AI agents and internal tooling that reduce bottlenecks in AIS’s own processes — cutting down time spent on repetitive evaluation, data, or experimentation work so teams can focus on the hard problems Partner with other ML engineers, software engineers, product managers, customers, data annotators, and Forward Deployed Engineers to take your work from idea to production and translate enterprise and government requirements into robust ML capabilities Set AI/ML technical direction, mentor senior and staff-track engineers and scientists across teams, and raise the bar on experimental rigor org-wide Requirements: 5+ years of experience as an ML engineer or applied/research scientist, including direct experience training or fine-tuning models in production systems PhD in Computer Science, Electrical Engineering, or a related field Broad, hands-on fluency across the agentic ML stack — model training and fine-tuning (SFT, RLHF/RLAIF, reward modeling), evaluation and observability infrastructure, and agent architecture (tool use, planning, memory, multi-agent orchestration) — with demonstrated depth or expertise in at least one area within the AI/ML domain Demonstrated ability to move across problem areas rather than specialize in one corner of the ML stack — comfortable picking up unfamiliar parts of a system quickly Track record of partnering with software engineers to productionize research and experimental work, not just deliver a one-off analysis — and of pushing code to production yourself when needed — with a genuine drive for pathfinding, 0-to-1 problems where the right approach isn’t yet known Track record of setting AI/ML technical direction — choosing methods and architectures that other teams adopt — and collaborating across functions (Product, Forward Deployed Engineering, etc.) to navigate ambiguous requirements and bring them to production Track record of mentoring engineers and scientists, giving and receiving direct, substantive technical feedback at a staff level, and influencing decisions and standards beyond your own team — through design reviews, technical writing, or shaping how other teams approach a problem Nice to have: Published research, open-source contributions, or patents in agent training methods, LLM alignment, or applied ML Experience with online learning, continuous fine-tuning, or automated data/curriculum generation from production traces Experience with model or systems optimization (e.g., training efficiency, latency, cost, or inference efficiency at scale) Experience working in regulated or enterprise/government contexts Track record of taking a novel training method or agent architecture from prototype to something running reliably in production, navigating ambiguity along the way Prior experience as a technical lead setting direction across multiple teams or problem areas 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: $250,000 — $350,000 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 accommodations@scale.com. 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.

Requirements

Must-haves

Build AI agents and internal tooling that reduce bottlenecks in AIS’s own processes — cutting down time spent on repetitive evaluation, data, or experimentation work so teams can focus on the hard problems
Set AI/ML technical direction, mentor senior and staff-track engineers and scientists across teams, and raise the bar on experimental rigor org-wide
5+ years of experience as an ML engineer or applied/research scientist, including direct experience training or fine-tuning models in production systems
PhD in Computer Science, Electrical Engineering, or a related field
Demonstrated ability to move across problem areas rather than specialize in one corner of the ML stack — comfortable picking up unfamiliar parts of a system quickly

Nice-to-haves

Published research, open-source contributions, or patents in agent training methods, LLM alignment, or applied ML
Experience with online learning, continuous fine-tuning, or automated data/curriculum generation from production traces
Experience with model or systems optimization (e.g., training efficiency, latency, cost, or inference efficiency at scale)
Experience working in regulated or enterprise/government contexts
Track record of taking a novel training method or agent architecture from prototype to something running reliably in production, navigating ambiguity along the way
Prior experience as a technical lead setting direction across multiple teams or problem areas

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learningllm

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

Pto