Staff/Senior Machine Learning Research Engineer

The shape of it

In the posting’s own words

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.

What it asks for · 7

• 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

Also a plus

• 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

Degree language

• PhD in Computer Science, Electrical Engineering, or a related field

Tools and skills named

Words the posting leans on

• production10×
• systems10×
• technical10×
• agent9×
• engineers9×
• problem8×
• research8×
• ais7×
• evaluation7×
• training7×
• experience6×
• infrastructure6×
• methods6×
• agentic5×
• architectures5×
• model5×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

More open at Scale AI

• AI Advisory ConsultantSan Francisco, CA; New York, NY
• AI Advisory PrincipalSan Francisco, CA; New York, NY
• AI Applications Ops Manager, GPSDoha, Qatar
• AI Builder InternSan Francisco, CA
• AI Infrastructure Engineer, Model Serving PlatformSan Francisco, CA; New York, NY
• AI Infrastructure Engineer, Sandbox PlatformSan Francisco, CA; Seattle, WA; New York, NY