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.
Claude serves millions of individual users alongside enterprise clients. Safeguards Engineering develops the foundational infrastructure that ensures its safe use: robust access controls, active abuse detection, real-time probes and classifiers within the inference pipeline, offline monitoring for review, and the underlying systems powering it all. Operating at scale and velocity, we continuously adapt to technology whose capabilities and risks evolve weekly.
The Safeguards Data team owns the data lifecycle for Safeguards—routing user data and safety metadata from online systems into offline storage and enabling access for downstream teams. Our goal is to provide privacy-safe access to sensitive data for approved uses while ensuring performance, reliability, and minimal blast radius. We prioritize security by design, embedding controls directly into systems operating across AWS, GCP, Azure, and customer-managed environments.
This is a systems engineering position within a small, newly established team. Rather than managing an existing backlog, you will have the autonomy to own a problem domain, establish its technical vision, and shape the team's trajectory.
Key responsibilities- Design, build, and operate the stores, pipelines, schemas, and APIs that Safeguards' detection, evaluation, investigation, and review systems read from.
- Build privacy-safe interfaces that allow ML training, evaluation, and investigation workflows to utilize sensitive data without requiring broad access to raw records, ensuring access is tied to purpose and built-in lineage.
- Scale the distributed storage, ingestion, and processing systems operated by Safeguards, maintaining cost efficiency, reliability, and portability across deployment environments.
- Collaborate with investigators, researchers, legal, privacy, and security teams to translate operational requirements into functional mechanisms and systems for safe data utilization.
- Uphold a high operational bar by participating in the on-call rotation, leading incident reviews, and continuously reducing manual operational overhead.
- Lead through technical design by authoring foundational proposals and conducting cross-team architectural reviews.
- Have strong systems fundamentals (processes, threads, RPC, storage, and how systems fail) and have built and operated large-scale, fault-tolerant distributed systems
- Have built and operated systems that handle sensitive or high-value data, with access, privacy, retention and security controls designed in from the start
- Have a track record of building platforms or internal tooling that other teams widely adopt
- Have led the technical design of projects that span several engineers or teams
- Learn quickly and adapt: you're comfortable with ambiguity, a fast pace, and systems or languages you haven't used before
- Care about the impact of your work and about making AI safe, use good judgment about what matters most, and can explain technical trade-offs to non-technical partners
- Proficiency in one or more systems languages (e.g., C, C++, Rust, Go)
- 8+ years of experience as a software engineer, and a Bachelor's degree in Computer Science, Software Engineering or comparable professional experience
- Experience building data infrastructure for ML: training datasets, offline batch inference or evaluation
- Experience with data governance requirements in a regulated or high-sensitivity domain
- Fluency with agentic coding tools such as Claude Code
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.
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.
Anthropic San Francisco, California, USA Office
548 Market St, San Francisco, California, United States, 94104
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