Staff+ Software Engineer, Safeguards Data

Anthropic · New York City, NY · Safeguards (Trust & Safety) · listed August 14, 2026

The shape of it

Seniority
Staff
Experience asked
8+ years
Where
Hybrid
Stated pay
$320,000 – $485,000 USD
Requirements listed
6
Length
1,115 words

In the posting’s own words

This is a systems engineering position within a small, newly established team. Rather than managing an existing backlog, you will have the autonomy to own a problem domain, establish its technical vision, and shape the team's trajectory.

What it asks for · 6

  • Have strong systems fundamentals (processes, threads, RPC, storage, and how systems fail) and have built and operated large-scale, fault-tolerant distributed systems
  • Have built and operated systems that handle sensitive or high-value data, with access, privacy, retention and security controls designed in from the start
  • Have a track record of building platforms or internal tooling that other teams widely adopt
  • Have led the technical design of projects that span several engineers or teams
  • Learn quickly and adapt: you're comfortable with ambiguity, a fast pace, and systems or languages you haven't used before
  • Care about the impact of your work and about making AI safe, use good judgment about what matters most, and can explain technical trade-offs to non-technical partners

Also a plus

  • Proficiency in one or more systems languages (e.g., C, C++, Rust, Go)
  • 8+ years of experience as a software engineer, and a Bachelor's degree in Computer Science, Software Engineering or comparable professional experience
  • Experience building data infrastructure for ML: training datasets, offline batch inference or evaluation
  • Experience with data governance requirements in a regulated or high-sensitivity domain
  • Fluency with agentic coding tools such as Claude Code

What the job covers

  • Design, build, and operate the stores, pipelines, schemas, and APIs that Safeguards' detection, evaluation, investigation, and review systems read from.
  • Build privacy-safe interfaces that allow ML training, evaluation, and investigation workflows to utilize sensitive data without requiring broad access to raw records, ensuring access is tied to purpose and built-in lineage.
  • Scale the distributed storage, ingestion, and processing systems operated by Safeguards, maintaining cost efficiency, reliability, and portability across deployment environments.
  • Collaborate with investigators, researchers, legal, privacy, and security teams to translate operational requirements into functional mechanisms and systems for safe data utilization.
  • Uphold a high operational bar by participating in the on-call rotation, leading incident reviews, and continuously reducing manual operational overhead.
  • Lead through technical design by authoring foundational proposals and conducting cross-team architectural reviews.

Tools and skills named

Cloud & infra
  • AWS
  • Azure
  • Distributed systems
  • GCP
Models & research
  • Inference2×
  • Machine learning2×
Languages
  • C++
  • Go
  • Rust
Security & compliance
  • Security3×
Ways of working
  • On-call

Words the posting leans on

  • systems13×
  • data9×
  • access6×
  • safeguards5×
  • design4×
  • experience4×
  • review4×
  • technical4×
  • controls3×
  • engineering3×
  • evaluation3×
  • offline3×
  • operated3×
  • operational3×
  • safe3×
  • security3×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

The posting, your resume, and the gaps between them. One click loads all three.

More open at Anthropic

every open role at Anthropic →

How this page was made

An automated read of a public job posting, fetched November 2, 2026 and last changed by Anthropic on October 21, 2026. Every list above is pulled from the posting’s own sentences — nothing rewritten, nothing added, no judgment about the role or the company. Counts and seniority are read off the text by rule, so they can be wrong where the posting is unusual. The original is the only thing that binds. Openings close without warning; check the source before spending an evening on it.