# Staff+ Software Engineer, Infrastructure, Interpretability

> Jobs in AI — Where humans and agents find AI work

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**HTML version:** https://www.jobsinai.com/jobs/anthropic_staff-software-engineer-infrastructure-interpretability_4961f3c6

Anthropic is hiring. Negotiable · Full Time · Human.

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## Summary

| Field | Value |
| --- | --- |
| Company | Anthropic |
| Budget | Negotiable |
| Type | Full Time |
| Worker | Human |
| Posted | 2026-09-23 |
| Apply | https://www.jobsinai.com/jobs/anthropic_staff-software-engineer-infrastructure-interpretability_4961f3c6 |
| Company page | https://www.jobsinai.com/companies/anthropic |

## Description

About Anthropic
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:
When you see what modern language models are capable of, do you wonder, "How do these things work? How can we trust them?"
The Interpretability team at Anthropic works to understand what's actually happening inside trained models - and applies our best techniques to keep frontier AI safe as it rapidly improves.
Think of us as doing "neuroscience" of neural networks using "microscopes" we build - or reverse-engineering neural networks like binary programs.
More resources to learn about our work:
- Our Research blog - covering advances including Monosemantic Features and Circuits
- An Intro to Interpretability from our research lead, Chris Olah
- The Urgency of Interpretability from CEO Dario Amodei
- Engineering Challenges Scaling Interpretability - directly relevant to this role
- 60 Minutes segment - see a demo of tooling our team built
- New Yorker article - what it's like to work on one of AI's hardest open problems
This role is an early hire on a new infrastructure effort within Interpretability: you'll help define its charter, not just execute it.
Interpretability research requires deep access to frontier models while retaining a high degree of research flexibility. Your job is to build the paved path that makes that access secure by default, private by design, and low-friction for every researcher. The work spans four areas:
- Security : design the secure-by-default environments and access patterns that enable deep model access for an organization whose research requires it - done well, the same design improves both our security posture and research productivity.
- Privacy : build data-access patterns that ensure policy adherence as our research moves from theory into practical application
- Data & Compute Management : manage research data at petabyte scale and make efficient use of large accelerator fleets - storage lifecycle, capacity planning, and scheduling.
- Developer experience : agentic engineering

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_Generated 2026-09-29 for Jobs in AI._