Engineering Manager, Integrity and Attestation

Engineering ManagerAnthropic'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 growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.The Integrity and Attestation team's remit is to prove what is running at Anthropic: what code it is, where it came from, and whether the machine underneath it is in a known-good state. We build the systems that cryptographically verify build artifacts, model weights, and machines, from hardware roots of trust through firmware and boot to the credentials a workload receives. We also secure the management plane of our servers. These controls protect our model weights and customer data across cloud providers and bare-metal AI data centers, including sites operated by partners.We are hiring an Engineering Manager to lead this team. Anthropic is bringing up new compute quickly, across new hardware platforms and new partners, and each one has to meet the same bar. In this role you will set the team's charter, practices, and priorities. You will partner with our infrastructure, data center, and hardware teams, as well as outside vendors, so that integrity is designed in rather than added later. The goal is simple to state: a machine gets secrets and workloads only after it proves it should, and a new site reaches that bar without slowing down compute.About the role:Own the integrity roadmap: threat model from silicon and firmware up through the operating system and workloads, decide what to build, and deliver itBe able to reason about the design and implementation of remote attestation, measured and verified boot, hardware roots of trust (TPM, SPDM), machine identity, and the reference values that define known-goodMake attestation the default path, so machines receive credentials and join clusters only after proving their firmware, system software, and security configuration, with no human in the loopBuild provenance for what we deploy: signed build artifacts and model weights, independent verification, and tamper-evident transparency logsLead the protection of sensitive data in use and at rest, including encryption and the chokepoints that control access to itSecure the server management plane across every site: scoped and audited access to baseboard management controllers and management of per-device credentialsMake security repeatable as compute grows, so a new site or hardware platform inherits these controls in hours, not weeksJoin compute bring-up, data center, and hardware teams at design timeDefine clear ownership with partner teams: when operations move to another team, your team keeps the security properties and can prove they still holdOwn the integrity evidence for our security goals and compliance: the invariants hold, and you can prove itHire, grow, and retain senior security software engineers; own their career development, and set the practices the team runs on as it scalesWho you are:Low ego, high empathy; has built diverse teams and has former reports who would vouch for them.Experience managing infrastructure or security engineering teams, including hiring, developing, and managing senior and staff engineersHas grown engineers into staff or lead roles, and experience with performance managementCreated and maintained a high performing in hypergrowth environments. Kept a team intact and delivering through reorgs, charter changes, or leadership turnoverExperience owning a security or infrastructure domain end to end: setting multi-year strategy, delivering year-long cross-organizational work with little direction, and answering to executives for its risk and tradeoffsAn appreciation for distributed systems and properties beyond security, including reliability, efficiency, and maintainabilityScaled a team and its systems together through hypergrowth, where last quarter's architecture was already too smallHands-on engineering in at least one of: platform security, secure boot and firmware, hardware roots of trust, confidential computing, or software supply chain integrityThreat modeling and risk assessment for systems that span hardware and software, with strong security judgment and pragmatismComfortable leading through ambiguity with multiple partner teams and outside vendors, where you don't control the hardware or the scheduleYou may be a good fit if you have:At least 5+ years managing engineering teams and 8+ years hands-on in platform, infrastructure, or security engineeringBuilt or operated remote attestation, device identity, or fleet-wide boot integrity at production scaleWorked with BMCs, Redfish, GPU or accelerator attestation, or large-scale bare-metal provisioningExperience with Nitro, SEV-SNP, TDX, or similar confidential computing platformsBuilt signing, provenance, or transparency systems such as Sigstore, SLSA, or binary transparencyDesigned key management or offline ceremonies for high-value secretsWorked in high-assurance environments where controls had to be evidenced, not just deployedTaken infrastructure from zero to one and then to maturity: built the first version where nothing existed, then run and hardened it into a system other teams depend onBuilt or rebuilt a hiring loop and held the bar while adding headcount quicklyPragmatic and steady: cares about unblocking research, product and compute as much as a clean architecture, and brings patience and judgment to opinionated senior engineersClear and persuasive communicator in writing and in personFamiliarity with AI security and safety conceptsThe 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: $405,000 —$405,000 USDLogisticsMinimum education: Bachelor's degree or an equivalent combination of education, training, and/or experienceRequired field of study: A field relevant to the role as demonstrated through coursework, training, or professional experienceMinimum years of experience: Years of experience required will correlate with the internal job level requirements for the positionLocation-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 DifferentWe 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