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.

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.