Staff+ Software Engineer, Distributed Systems

Anthropic•New York City, NY

1d•Hybrid

About The Position

Anthropic is seeking software engineers to join their Safeguards team, focusing on building safety and oversight mechanisms for AI systems. This role involves monitoring models, preventing misuse, and ensuring user well-being by developing distributed and scalable systems to detect unwanted model behaviors and prevent disallowed use. The engineer will apply technical skills to uphold principles of safety, transparency, and oversight while enforcing terms of service and acceptable use policies.

Requirements

Bachelor’s degree in Computer Science, Software Engineering or comparable experience
Proficiency in Python, Distributed Systems, and Infrastructure as Code
Have worked across multiple cloud providers, or built infrastructure designed to be provider-agnostic
Have built and operated large-scale, distributed infrastructure, such as data platforms, control planes, or job schedulers
Have experience with sandboxing and isolation technologies (containers, microVMs, network policy) or with a systems language such as Rust
Strong communication skills and ability to explain complex technical concepts to non-technical stakeholders

Nice To Haves

8+ years of experience in a software engineering position, not including internships
Have built systems that handle sensitive or regulated data, with requirements around access control, auditability, retention, and data residency
Have experience running LLM-based agents in production
Have experience with integrity, spam, fraud, or abuse detection and mitigation

Responsibilities

Independently scope and lead complex, multi-month infrastructure projects, from an ambiguous starting point through to a production system
Develop monitoring systems to detect unwanted behaviors from our API partners and potentially take automated enforcement actions; surface these in internal dashboards to analysts for manual review
Stand up and run deployments of those monitoring systems across multiple clouds, including inside cloud-provider partner environments where data residency laws apply. Keep the deployments consistent through shared deployment pipelines, smoke tests, observability, and alerting
Design and harden the sandboxed runtime that AI agents execute in: isolation, network egress controls, least-privilege data access, audit logging, and insider-risk controls
Manage cost and capacity for large volumes of long-running agent work, and set and meet service-level objectives for the platform
Partner with the engineers and researchers who write and evaluate the monitoring agents, and with security, privacy, and legal teams, so new detection work can ship quickly on a platform everyone trusts