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San Francisco, California, United States
Hybrid
Available
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.
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, tooling and observability that keep researchers moving fast
In this role, you’ll be deeply embedded alongside Interp Researchers to understand their workflows - building your understanding of the research as you go; at the same time you’ll bridge communication with Anthropic’s wider platform and security teams.. Every hour of researcher friction you remove is multiplied across the whole organization, and the infrastructure you build sets the pace at which interpretability results reach real safety decisions.
Design, build, and own shared infrastructure for Interpretability - research environments, data systems, and compute tooling that researchers rely on daily
Lead cross-team efforts with our agentic engineering, security, compute, and storage platform teams, so that company-wide solutions serve research needs
Discover and resolve major organization-wide developer experience issues
Help take interpretability methods from research code to dependable audit pipelines
Are highly proficient in at least one programming language (e.g., Python, Rust, Go, Java) and productive with Python
Have significant experience building and operating secure and scalable software infrastructure - cloud systems, distributed systems, or developer tooling
Have strong cross-functional communication skills - equally at home working with researchers and with platform and security teams
Are extremely curious about unfamiliar domains
Have a strong ability to prioritize the most impactful work and are comfortable operating with ambiguity and questioning assumptions
Are curious about interpretability research and its role in AI safety (though no research experience is required!)
Care about the societal impacts and ethics of your work
Experience with cloud infrastructure (e.g. GCP or AWS), Kubernetes, networking and infrastructure-as-code
Security engineering experience: identity / auth / access management, sandboxing, red teaming
Experience with data warehousing, large-scale storage systems, and data lifecycle management - especially for research
Experience with compute schedulers and accelerator fleet management
Experience building developer productivity tooling and observability stacks
Experience building tooling to accelerate research teams
Design and stand up a hardened research environment where researchers experiment directly on frontier model weights
Build lifecycle management for petabytes of research data - visibility, retention, and cost efficiency
Build self-serve scheduling and capacity tooling
Create the observability that catch infrastructure regressions before they cost researchers valuable time
This role is based in the San Francisco office; however, we are open to considering exceptional candidates for remote work on a case-by-case basis.
The annual compensation range for this role is listed below.
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.
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
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.
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.
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.