Manager I, Engineering - Code Intelligence

The shape of it

In the posting’s own words

Code Intelligence sits at the intersection of agentic engineering and product, running agents against real customer source code in production. This is a chance to own an AI-native team at the center of a business-critical growth area. High visibility, high leverage, and squarely in the fastest-growing part of the AI agent space. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them.

What it asks for · 6

• Experienced engineering manager with a track record of shipping products with direct customer interaction, not just internal stakeholders.
• Hands-on experience building scaled LLM software: LLM APIs, agent frameworks, prompt and eval tooling, harness and tool-use development, and closed-loop evaluation systems including LLM-as-judge.
• Strong product and customer mindset — you put solving customer problems first and know how to ship and iterate quickly.
• Comfortable setting your own product strategy in an ambiguous environment rather than executing a pre-defined roadmap.
• SDLC and developer tooling background is a plus but not required — strong AI and agent experience is what matters most.
• Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you're passionate about technology and want to grow your skills, we encourage you to apply.

What the job covers

• Lead and develop an existing engineering team, building trust, setting technical direction, and establishing a high bar for ownership and execution.
• Own the roadmap and execution across four areas: source code indexing, PR Agentic Reviewers (agents that generate artifacts like metric descriptions on customer pull requests), shift-left work (extending code-writing agents to make direct changes to customer repos), and closed-loop feedback and evaluation systems including LLM-as-judge.
• Set the team's product strategy independently while partnering day-to-day with product teams company-wide — this team functions as its own product org.
• Build and operate scaled LLM and agent software in production: LLM APIs, agent frameworks, harness and tool-use layers, prompt and eval tooling, and closed-loop evaluation systems that measure and improve agent output quality.
• Partner closely with customers and internal stakeholders to deeply understand needs and translate them into technical direction.
• Drive the team's AI-native development practices, setting high standards for safety, validation, and increasing agent autonomy over time.

Tools and skills named

Words the posting leans on

• agent10×
• customer7×
• product7×
• llm4×
• engineering3×
• evaluation systems3×
• setting3×
• agent frameworks2×
• agentic2×
• ai-native2×
• apis agent2×
• area2×
• build2×
• building2×
• closed-loop evaluation2×
• development2×

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.

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