[DH] Engineering Manager, AI Observability

San Francisco · USD 405,000-850,000 / year · 5+ years experience · Hybrid · Posted Sep 23, 2026

At a glance

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

As an Engineering Manager for the team, you'll lead research engineers who design and build systems that let AI analyze large, unstructured datasets and produce structured, trustworthy insights.
This is a high-leverage role where the tools you build will be used by dozens of researchers and investigators to directly shape our ability to measure and mitigate both misuse and misalignment.

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

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

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

Location-based hybrid policy: expect all staff to be in one of our offices at least 25% of the time, though some roles may require more time in our offices.