Responsibilities

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.
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

Qualification

PhD in Computer ScienceBroadPublished researchExperience with online learning

Required

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

Preferred

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

Senior Machine Learning Engineer, Agent Oversight at Scale AI · LumiJobs

Senior Machine Learning Engineer, Agent Oversight

Responsibilities

Qualification

Required

Preferred

Benefits

Company

Scale AI

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