The opportunity
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…
What you'll do
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
+ years of experience as an ML engineer or applied/research scientist,: including direct experience training or fine-tuning models in production systems
What they're looking for
- 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
