[DH] Engineering Manager, AI Observability

Anthropic · San Francisco, CA · AI Research & Engineering · listed September 23, 2026

The shape of it

Seniority
Manager
Experience asked
2–5 years
Where
Hybrid
Stated pay
$405,000 – $850,000 USD
Requirements listed
7
Length
1,395 words

In the posting’s own words

As AI training and deployments scale, the volume of data we need to monitor and understand is exploding. Our team uses Claude itself to make sense of this data. We own an integrated set of tools enabling Anthropic to ask open-ended questions, surface unexpected patterns, and maintain meaningful human oversight over massive datasets. Our tools are widely adopted internally — powering ongoing enforcement , threat intelligence investigations , model audits , and more — and we're looking for an experienced engineering manager to help us both scale up existing applications and go zero-to-one on new ones.

What it asks for · 7

  • Are an experienced manager (at least 2 years) and actively enjoy people management
  • Have 5+ years of software engineering experience, with meaningful exposure to ML systems
  • Are excited about the problem of scaling human oversight of AI systems
  • Are familiar with LLM application development (context engineering, evaluation, orchestration)
  • Enjoy building tools that other people use — you care about UX, reliability, and documentation
  • Can context-switch between deep infrastructure work and user-facing product thinking
  • Thrive in collaborative, cross-functional environments

Also a plus

  • Research experience in AI safety, alignment, or responsible deployment
  • Strong people management experience: coaching, performance evaluation, mentorship, career development
  • Experience recruiting for your team: predicting staffing needs, designing interview loops, evaluating candidates, and closing them
  • Practical experience with both data science and engineering, including developing and using large-scale data processing frameworks
  • Experience with productionizing internal tools or building developer-facing platforms
  • Background in building monitoring or observability systems
  • Comfort with ambiguity — our team is small and growing, and you'll help define what we become

What the job covers

  • Lead the design and implementation of AI-based monitoring systems for AI training and deployment
  • Extend and improve core frameworks for processing large volumes of unstructured text
  • Partner with researchers and safety teams across Anthropic to understand their analytical needs, and prioritize the team's work to build solutions
  • Develop agentic integrations that allow AI systems to autonomously investigate and act on analytical findings
  • Contribute to the strategic direction of the team, including decisions about what to build, what to partner on, and where to invest
  • Coach and support your reports to understand and pursue their professional growth
  • Run the team's recruiting efforts, ensuring we can grow as quickly as we need
  • Design processes that help the team operate effectively

Tools and skills named

Ways of working
  • Mentorship4×
  • Cross-functional2×
  • Technical writing2×
Models & research
  • LLM2×
  • Machine learning2×
Operations & finance
  • Recruiting4×
Cloud & infra
  • Observability2×
Product & design
  • User experience2×

Words the posting leans on

  • experience12×
  • systems11×
  • engineering8×
  • tools7×
  • build6×
  • building6×
  • data6×
  • deployment5×
  • design5×
  • frameworks5×
  • understand5×
  • analytical4×
  • development4×
  • enjoy4×
  • evaluation4×
  • manager4×

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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How this page was made

An automated read of a public job posting, fetched September 23, 2026 and last changed by Anthropic on September 23, 2026. Every list above is pulled from the posting’s own sentences — nothing rewritten, nothing added, no judgment about the role or the company. Counts and seniority are read off the text by rule, so they can be wrong where the posting is unusual. The original is the only thing that binds. Openings close without warning; check the source before spending an evening on it.