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Salary: USD 405,000 - 850,000 / annual
Anthropic is seeking an Engineering Manager to lead the AI Observability team, which builds systems that enable AI to analyze large, unstructured datasets and produce structured, trustworthy insights. The team works across the full stack—from core analysis frameworks to user-facing applications—and their tools are widely adopted internally for enforcement, threat intelligence, model audits, and safety investigations.
In this role, you will lead research engineers designing and building systems that process tens or hundreds of thousands of conversations and documents. You'll be responsible for the strategic direction of the team, including decisions about what to build, what to partner on, and where to invest. You'll partner with researchers and safety teams across Anthropic to understand their analytical needs and prioritize the team's work accordingly.
Key responsibilities include:
- Leading the design and implementation of AI-based monitoring systems for AI training and deployment
- Extending and improving core frameworks for processing large volumes of unstructured text
- Developing agentic integrations that allow AI systems to autonomously investigate and act on analytical findings
- Coaching and supporting reports on professional growth
- Running the team's recruiting efforts to enable rapid scaling
- Designing processes that help the team operate effectively
- Contributing to strategic decisions about team direction and investment
This is a high-leverage role where the tools you build will be used by dozens of researchers and investigators, directly shaping Anthropic's ability to measure and mitigate both misuse and misalignment.
Anthropus is a public benefit corporation headquartered in San Francisco, focused on creating reliable, interpretable, and steerable AI systems. The company operates as a single cohesive team on large-scale research efforts, valuing impact and communication skills. They offer competitive compensation, optional equity donation matching, generous vacation and parental leave, flexible working hours, and collaborative office space. The role requires at least 25% in-office presence, though some roles may require more.
REQUIREMENTS:
- At least 2 years of management experience with active enjoyment of people management
- 5+ years of software engineering experience with meaningful exposure to ML systems
- Familiarity with LLM application development (context engineering, evaluation, orchestration)
- Bachelor's degree or equivalent combination of education, training, and professional experience in a field relevant to the role
- Excitement about scaling human oversight of AI systems
- Ability to context-switch between deep infrastructure work and user-facing product thinking
- Thriving in collaborative, cross-functional environments
STRONG CANDIDATES MAY ALSO HAVE:
- Research experience in AI safety, alignment, or responsible deployment
- Strong people management experience including coaching, performance evaluation, mentorship, and career development
- Experience recruiting for teams: predicting staffing needs, designing interview loops, evaluating and closing candidates
- Practical experience with both data science and engineering, including large-scale data processing frameworks
- Experience productionizing internal tools or building developer-facing platforms
- Background in building monitoring or observability systems
- Comfort with ambiguity in a small, growing team environment
