Engineering Manager, Safeguards Interventions

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. About the role The Safeguards team is responsible for ensuring our models and products are developed and deployed safely. We're looking for an Engineering Manager to lead the Interventions team: the group responsible for what happens when a safety system fires. It owns the composable arsenal of systems that sit between our detection stack (classifiers and probes) and the user, across every Anthropic surface: 1P products, the API, and third-party clouds. This includes inline interventions for areas like bio, cyber, and acceptable usage as well as downstream areas like child safety and copyright. This team is responsible for ensuring that we evolve and drive the quality of our interventions to enable our products to grow safely. Key responsibilities · Hands-on lead and grow a team of engineers; own roadmap, OKRs, and execution. · Drive cross-functional work with ML Infra, Research, Product, Policy, and Legal - and with cloud partners for 3P deployment. · Set the bar for when an intervention is good enough to ship - backed by measurement - and represent safety and product tradeoffs to leadership and external stakeholders. · Own production reliability for intervention and compliance systems: incident response, postmortems, SLOs, and the verification processes that prevent repeat incidents. Minimum qualifications · Have managed engineering teams shipping production ML or safety-enforcement systems where the system's decisions directly affected users. · Have run high-stakes, compliance-adjacent production systems: comfortable with on-call, incidents, regulator-driven requirements, and building the process scaffolding that prevents recurrence. · Care about measurement: you've built (or insisted on) the evals that prove a system does what it claims, and you've killed things that didn't. · Can drive ambiguous, multi-stakeholder tradeoffs (safety vs UX vs latency vs cost) to a decision and own the outcome. · Care deeply about AI safety and want your team's work to be the reason advanced models can be deployed at all. Preferred qualifications · Have worked in trust & safety, integrity, or abuse-prevention engineering at scale. · Experience with compliance-driven systems (child safety, copyright, age assurance) and the legal/policy interfaces they require. · Have shipped systems across multiple cloud providers and understand the parity/verification problems that creates. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $405,000—$485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.