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Staff + Software Security Engineer, Labs
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

Frontier AI is on track to be among the most consequential and most adversarially-targeted technology in the world. The capability curve is steep, the adversaries who want these systems are extremely well-resourced, and the security bar this will eventually require is well beyond where the industry operates today. Incremental hardening alone is not going to close that gap, so we need breakthroughs and a group of people to go find them.

Security Labs is that team. We run a portfolio of high-risk, high-expected-value security projects: the work that seems impractical until someone optimistic and stubborn enough actually tries it. Projects run on the order of weeks rather than quarters, and each one is either handed off to the Anthropic team that will own it in production or wound down with a writeup of what we learned. We expect a meaningful fraction of our bets not to land.

This team is intentionally scrappy and ambitious. We're intentionally iterating fast on high-risk research bets and we expect a meaningful fraction not to land. If a 30% project success rate sounds uncomfortable, or uncharted and chaotic territory isn’t both frightening and exciting, this probably isn't the right fit. There are other places in Anthropic Security doing important work with more structure, less risk, and more certain paths to positive outcomes.

The questions we're trying to answer include:

• Can our core research workflows survive extreme isolation?

• Can we replace trust with cryptographic guarantees?

• Can the models themselves become our most effective security control?

• What would it actually take to defend against a tier-1 state adversary, and how much of that can we build now?

Who we're looking for. We're hiring generalists with rare depth. You're a strong software engineer as a baseline, and on top of that you've gone deep in at least one area most engineers don't get near: firmware or hardware security, applied cryptography, OS / kernel / hypervisor internals, formal methods, reverse engineering, or high-assurance and cross-domain systems. You've built things under your own direction, you're comfortable jumping layers when the problem demands it, and you'd rather take a swing at something that might not work than ship the safe incremental thing. You think the trajectory of AI matters a great deal, you're not comfortable with how the security side of it is going by default, and you'd rather be on the inside building the answer than watching from outside.

Current Project Areas

The portfolio changes as we learn. The kinds of bets currently in flight or queued:

• Standing up a prototype high-assurance research cluster: running real Anthropic training and research workloads under extreme isolation and physical security controls, and finding out exactly where productivity breaks and what we'd need to invent to get it back

• Provable inference: cryptographic verification (zero-knowledge proofs, attestation chains) that a given output came from a specific model running specific code, replacing "trust us" with math

• Build hypervisor-isolated microVM workload isolation across the fleet

• Compiling an ML kernel through a formally verified pipeline where every lowering step carries a machine-checked proof of equivalence, making compilation-layer sabotage mathematically detectable

• Regenerating clusters: automation that spins up a purpose-built cell, runs a workload, and tears the whole thing down on a TTL measured in hours, so attacker persistence has an expiry date

• Using Claude itself to drive security work end to end: threat modeling new compute platforms, rewriting critical services to zero external dependencies, running the test equipment that validates what hardware datasheets claim

Part of your job is deciding what comes next. We hire people we trust to pick good bets, and project selection is owned by the engineers doing the work.

Key responsibilities

• Own Security Labs projects end to end. You'll scope the bet, build the prototype, run it against real workloads, and bring it to either a hand-off or a documented exit

• Stand up novel security infrastructure fast (isolated clusters, attestation chains, hypervisor and runtime work, verification tooling) optimizing for what we learn rather than for permanence

• Find the receiving team early, bring them along while you build, and hand them something they actually want to own

• Work embedded with research and infrastructure teams (Pretraining, RL, Inference, Compute) to test whether their workflows survive what you're proposing, and document precisely where they don't

• Turn experimental results into short writeups that shape Anthropic's long-term security architecture, and into costed contingency plans we could execute on short notice

• Help pick the next round of bets and influence the industry to get better along the way

Minimum qualifications

• Genuinely care about where AI is heading and want to work on the security problems that determine whether it goes well. This is the most important thing on this list

• Have real depth in at least one area most software engineers don't touch (e.g. firmware or hardware security, applied cryptography, OS / kernel / hypervisor internals, formal methods and verification, reverse engineering and exploit development, or high-assurance / cross-domain systems)

• Have built and shipped things under your own direction. Maybe you founded a company or research group, maintained an open-source project other people depend on, or shipped research that changed how people in your field work. We weight this far more than where you've worked or for how long

• Have a track record of choosing the problem yourself and seeing it through, rather than only executing a plan someone else handed you

• Are comfortable jumping between domains and layers of the stack when the problem calls for it, and have actually done so

• Have run prototypes or experiments where the goal was answering a hard question rather than shipping a permanent system, including ones that didn't pan out

• Write clearly enough to turn weeks of work into a couple of pages someone can act on

• Change your mind when the evidence says to, and are fine being the least-expert person in a room full of specialists

• Care about defense. Plenty of folks here come from offense and that background is valuable, but what you actually want to spend your time on now is making systems hold up

• Are a strong programmer (Python plus at least one of Rust, Go, or C/C++) and can stand up real infrastructure without that being the interesting part of your week

Preferred qualifications

• Experience inside airgapped or high-side environments (classified networks, cross-domain solutions, ICS/SCADA, financial trading infrastructure) and the operational realities of working in them

• A background in offensive security, red teaming, or vulnerability research, with calibrated intuitions for which threats actually matter

• Familiarity with ML infrastructure (training pipelines, distributed schedulers, inference serving, accelerator hardware) sufficient for grounded conversations with researchers about what their workloads actually need

• A history of working in environments built around rapid iteration rather than rigid change control: startups, applied research groups, independent consulting, small security shops

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.

Annual Salary:
$320,000 — $485,000 USD

Logistics

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.

For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act.

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.

How we're different

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.

Come work with us!

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.

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Requirements found

Training (must) Writing (must) Python (nice) Sales (nice) Recruiting

What this posting asks for

Level: Lead / staff — The title says "Staff".

Main responsibilities

Own Security Labs projects end to end. You'll scope the bet, build the prototype, run it against real workloads, and bring it to either a hand-off or a docum…
Stand up novel security infrastructure fast (isolated clusters, attestation chains, hypervisor and runtime work, verification tooling) optimizing for what we…
Find the receiving team early, bring them along while you build, and hand them something they actually want to own
Work embedded with research and infrastructure teams (Pretraining, RL, Inference, Compute) to test whether their workflows survive what you're proposing, and…
Turn experimental results into short writeups that shape Anthropic's long-term security architecture, and into costed contingency plans we could execute on s…
Help pick the next round of bets and influence the industry to get better along the way
Genuinely care about where AI is heading and want to work on the security problems that determine whether it goes well. This is the most important thing on t…
Have real depth in at least one area most software engineers don't touch (e.g. firmware or hardware security, applied cryptography, OS / kernel / hypervisor …
Have built and shipped things under your own direction. Maybe you founded a company or research group, maintained an open-source project other people depend …
Have a track record of choosing the problem yourself and seeing it through, rather than only executing a plan someone else handed you

Competencies an interviewer will look for

Technical skill Core
In the posting: “You're a strong software engineer as a baseline, and on top of that you've gone deep in at least one area most engineers don't get near: firmware or hardware…” · “Provable inference: cryptographic verification (zero-knowledge proofs, attestation chains) that a given output came from a specific model running specific co…” · “Using Claude itself to drive security work end to end: threat modeling new compute platforms, rewriting critical services to zero external dependencies, runn…”
Show it by: Names the tools, languages or systems used and how; Explains a technical choice and its trade-off.
Teamwork and collaboration Core
In the posting: “In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic.” · “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.” · “We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely…”
Show it by: Says what they did inside a team, not only what 'we' did; Shows how a disagreement was handled.
Ownership and accountability Core
In the posting: “Using Claude itself to drive security work end to end: threat modeling new compute platforms, rewriting critical services to zero external dependencies, runn…” · “Own Security Labs projects end to end.” · “A history of working in environments built around rapid iteration rather than rigid change control: startups, applied research groups, independent consulting…”
Show it by: Takes a task from start to finish without being chased; Owns a mistake and what was changed after it.
Communication Core
In the posting: “If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.” · “As such, we greatly value communication skills.”
Show it by: Adapts the message to the listener; Gives an example of explaining something clearly in writing or speech.
Planning and organisation Core
In the posting: “Have a track record of choosing the problem yourself and seeing it through, rather than only executing a plan someone else handed you” · “Familiarity with ML infrastructure (training pipelines, distributed schedulers, inference serving, accelerator hardware) sufficient for grounded conversation…”
Show it by: Shows how priorities were set when everything was urgent; Mentions deadlines met and how.
Attention to detail and quality Core
In the posting: “Your safety matters to us.” · “This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, S…”
Show it by: Gives an example of an error caught before it caused harm; Names the check or standard they used.
Adaptability and learning Supporting
In the posting: “We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely…”
Show it by: Describes learning something new quickly and using it; Shows a change of plan and how they adjusted.
Stakeholder management Supporting
In the posting: “Help pick the next round of bets and influence the industry to get better along the way”
Show it by: Shows how they aligned people with different goals; Explains how they handled a 'no' from someone senior.

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Practice questions

Tell me about yourself. Dedicated step-by-step practice.
If an interviewer asks something improper How to respond to questions about age, family, origin or health.
Built from this posting (8):
Walk me through a piece of work where you used your strongest technical skill. What exactly did you do? From this posting
Describe a time you worked with another team to deliver something. What was your part? From this posting
Tell me about a task you took from start to finish without anyone asking you to. From this posting
Tell me about a time you had to explain something complex to someone outside your field. From this posting
How have you used training in your work? Walk me through one real example. From this posting
Tell me about a problem you solved using writing. From this posting
Tell me about a time you helped someone on your team get better at their work. From this posting
Describe a time your plans changed suddenly. How did you adjust? From this posting
This posting reads as Software engineering: its questions come first.
Walk me through a feature you built from start to finish. What tools and languages did you use? Software engineering
Tell me about the hardest bug you have tracked down. How did you find the cause? Software engineering
Describe a technical design decision you made and the trade-off behind it. Software engineering
Tell me about a time you disagreed with a teammate in a code review. Software engineering
Production is down and you are the first engineer to notice. What do you do in the first 30 minutes? Software engineering
How do you decide what to test before you ship a change? Software engineering
Tell me about a time you helped a less experienced engineer get unstuck. Software engineering
Your estimate for a feature turns out to be badly wrong halfway through. What do you do? Software engineering
Tell me about a sale or agreement you are proud of.
Describe a time you handled an upset customer or colleague.
Tell me about a time you had several deadlines at once. How did you decide what to do first?
Give an example of a mistake you caught before it caused a problem.
Tell me about a time you taught someone how to do something.
Describe a time you took the lead without being asked.
Tell me about a time you had to explain something complicated in simple terms.
Describe a process you improved.
Do you have any questions for us?
Why do you want to work here?
Why is there a gap in your employment? Difficult question
Why did you leave your last job? Difficult question
Why are you changing careers? Difficult question
What is your greatest weakness? Difficult question
What are your salary expectations? Difficult question
You seem overqualified for this role. Why do you want it? Difficult question
Improper-question practice: How old are you? / Do you have children? / Where are you from originally?
A customer is angry about a delay you did not cause. What do you do?
How do you keep track of many open customer requests?
Tell me about a time you could not give a customer what they wanted.
You find that the inventory count does not match the system. What do you do?
Tell me about a time a shipment or delivery went wrong.
How do you make sure safety rules are followed on a busy shift?
Two managers ask you to book the same room at the same time. What do you do?
How have you used spreadsheets in your work?
Tell me about a time you had to enter or check a lot of data accurately.
A prospect says your price is too high. How do you respond?
How do you keep your pipeline organised?
Tell me about a technical problem you solved on your own.
You find a bug right before a release. What do you do?
Walk me through how you would check whether a report's numbers are right.