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Staff/Senior Machine Learning Research Engineer, Intelligent Systems

Scale AI· Applications Platform Engineering
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Hybrid (San Francisco, CA), Hybrid (New York, NY)$250k - $350kfull-timePosted 3mo ago

About the role

Staff-level ML research/engineering role across AIS to advance agentic AI in production, including training/fine-tuning, evaluation, memory and retrieval, tooling, and setting AI/ML technical direction.

Responsibilities

  • 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
  • Partner with other ML engineers, software engineers, product managers, customers, data annotators, and Forward Deployed Engineers to take work from idea to production and translate enterprise/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

Qualifications

  • 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
  • Track record of partnering with software engineers to productionize research and experimental work, and of pushing code to production yourself
  • Track record of setting AI/ML technical direction and collaborating across functions to navigate ambiguous requirements
  • Track record of mentoring engineers and scientists, providing substantive technical feedback, and influencing decisions/standards beyond own team

Skills

Required:ML model training and fine-tuningEvaluation and observability infrastructureAgent architecture (tool use, planning, memory, multi-agent orchestration)Cross-functional collaborationMentoring and technical leadership

Benefits

  • comprehensive health, dental and vision coverage
  • retirement benefits
  • learning and development stipend
  • generous PTO
  • potential commuter stipend
Last updated Sep 16, 2026. Interested? Apply on the company site.
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About Scale AI

Data-centric infrastructure to accelerate the development of AI

Scale accelerates the development of AI within organizations of any size to deliver critical business insights and operational efficiency. Its data-centric infrastructure platform leverages RLHF (Reinforced Learning with Human Feedback) to help organizations build the strongest AI models that supercharge their business, with customers across industries including Meta, Microsoft, U.S. Army, DoD’s Defense Innovation Unit, Open AI, General Motors, Toyota Research Institute, Brex, Instacart and Flexport.

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