Software Engineer, Cloud Inference Safeguards

Anthropic•Seattle, WA

8d•Hybrid

About The Position

We are seeking a Software Engineer to build and operate the safety, oversight, and intervention mechanisms that protect Claude on third-party cloud service provider (CSP) platforms. As the engineer responsible for Safeguards on those surfaces, you will ensure that every request served through our CSP partners is monitored for misuse, enforced against policy, and compliant with the data residency and privacy commitments that enterprise CSP customers expect. You will sit at the seam between the Safeguards organization and the Cloud Inference team: taking classifiers, detection signals, and enforcement policies developed by Safeguards and making them run reliably inside a CSP partner’s infrastructure at serving-path latency and scale. You will own the architecture that lets our safeguards operate within those constraints without gaps. You will build, deploy and operate the multi-layered defenses that catch unwanted model behavior in real time, the telemetry pipelines that give us situational awareness over CSP traffic, and the enforcement hooks that let us act quickly when something goes wrong. Your work will directly determine whether Anthropic can ship frontier models on CSP platforms at the same safety bar we hold ourselves to on our first-party API.

Requirements

Have a Bachelor’s degree in Computer Science, Software Engineering, or comparable experience
Have 4–10+ years of experience in high-scale, high-reliability software development, ideally with exposure to trust & safety, anti-abuse, fraud, or integrity systems
Are proficient in Python and comfortable working across the stack—from request-path services to data pipelines to internal tooling
Think adversarially: you can see a system from a bad actor’s perspective, anticipate how they will respond to countermeasures, and design defenses in depth rather than single points of enforcement
Have experience scaling infrastructure to accommodate rapid traffic growth while keeping latency and reliability within tight budgets
Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development
Have strong communication skills and can explain complex technical and risk tradeoffs to non-technical stakeholders across Policy, Legal, and partner organizations
Enjoy working in a fast-paced, early environment; comfortable with adapting priorities as driven by the rapidly evolving AI space

Nice To Haves

Building trust and safety, anti-spam, fraud, or abuse detection and mitigation mechanisms for AI/ML systems, or the infrastructure to support these systems at scale
Machine learning serving infrastructure (GPUs/TPUs, inference servers, load balancing) and the operational realities of running models in production
Major cloud platform internals—IAM, Network/service perimeter controls, regional resource constraints, cloud-native logging/monitoring—or experience shipping software that runs inside a partner’s cloud rather than your own
Data residency, privacy engineering, or compliance-constrained architectures, particularly where telemetry has to stay within regional or contractual boundaries
Working closely with operational and human-review teams to build custom internal tooling, admin UX, and alerting
Adversarial mindset: has shipped defenses against motivated attackers before, knows what it feels like when they adapt, and can sprint to close a gap before it becomes an incident
Comfortable operating at the intersection of platform/infra engineering and trust & safety—neither a pure infra engineer nor a pure T&S engineer, but someone who can credibly do both
Has shipped software that runs inside someone else’s infrastructure (partner cloud, embedded deployment, or similar) and knows how to get things done when you don’t control the whole stack
Senior enough to own a cross-team seam independently, drive consensus across orgs, and make latency/safety tradeoff calls without escalation
TypeScript or Rust, and agentic coding tools such as Claude Code

Responsibilities

Build, deploy and operate real-time safeguards infrastructure—classifiers, rate limits, enforcement actions, and intervention hooks—embedded directly in the third-party CSP inference serving path
Design and maintain the data residency and privacy architecture for safeguards signals on CSP platforms, ensuring we can detect abuse and monitor model behavior while honoring regionalization boundaries and enterprise contractual commitments
Develop telemetry, logging, and evaluation pipelines that give Safeguards, Policy, and T&S operational teams situational awareness over CSP traffic and close the visibility gap between third-party and first-party serving
Dive into the CSP serving stack to identify the lowest-impact points to gather signals or introduce interventions without degrading latency, stability, or overall architecture
Hold a high operational bar: own on-call, drive root-cause analyses and postmortems for safeguards incidents on CSP platforms, and build systems that reduce the human intervention required to keep Claude safe
Work closely with Safeguards research, Policy & Enforcement, the Cloud Inference team, and CSP partner contacts to turn detection research and policy decisions into production enforcement that works inside a partner’s cloud.