Staff Applied Scientist - Agentic Interfaces

Team Description

At Datadog, AI agents are becoming first-class consumers of observability, security, and software delivery data — from third-party coding agents like Claude Code, Cursor, and Copilot, to our own Bits SRE, Bits Assistant, and Bits Dev Agent. The Agentic Interfaces team owns the platform that connects these agents to Datadog: the MCP Server, the tools and retrieval surfaces agents call into, and — critically — the evaluation systems that tell us whether an agent's experience on Datadog data is actually getting better over time.

We're hiring a Staff Applied Scientist to define what "good" means for an Agentic interface at Datadog and to build the measurement systems that make it true. You'll design the evals, build the datasets, define the metrics, and partner with the AI engineers on the team to land the platform that lets every product group at Datadog ship integrations that are demonstrably better release over release.

What You’ll Do

• Own the evaluation strategy for Datadog's AI agent integrations. Define the metrics — offline and online, quality and cost, single-turn and trajectory-level — that the team and the broader organization optimize against.
• Build the eval datasets, golden traces, and regression harnesses that catch quality changes before they hit customers, and make those assets reusable by every team contributing tools to the platform.
• Drive measurable improvements to retrieval relevance, tool-selection accuracy, and context efficiency, partnering closely with the AI engineers on the team who build the underlying platform.
• Run applied research on the open problems in agent–data interaction: tool selection under large catalogs, multi-turn agent evaluation, grounding and hallucination control on live telemetry, cost/quality tradeoffs at scale.
• Partner with the Bits SRE, Bits Assistant, and Bits Dev Agent teams so first-party agents benefit from the same measurement substrate as third-party integrations, and so learnings move freely in both directions.
• Provide technical leadership across the Agentic Interfaces team and the broader organization through design reviews, working groups, and mentorship, and represent the team externally through talks, blog posts, and contributions to the open agent ecosystem.

Who You Are

• BS/MS/PhD in a scientific field, or equivalent experience.
• 10+ years of relevant engineering or applied science experience, including time as a technical lead.
• Proven track record of leading ML or GenAI initiatives in a product-driven environment, from research through production.
• Significant experience with evaluation, experimentation, or measurement of ML systems at scale.
• Strong product mindset and comfortable driving initiatives across cross-functional teams.
• Thrives in ambiguity and can make sound technical calls when the path isn't yet defined.
• ITAR access: Role requires access to ITAR-controlled data.