Senior Machine Learning Research Engineer

Навыки

• AI-инструменты в работе
• Дообучение моделей
• LLM
• Machine Learning
• Мониторинг и observability
• Роадмап
• Смарт-контракты

О компании и продукте

• 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.
• 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.

Задачи

• 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

Требования

• 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

Будет плюсом

• 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
• 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
• 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

Условия

• 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
• 216,000 — $270,000 USD

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