About the role:

The Safeguards ML Sampling Path team builds and operates the production services that power Claude's safety systems. These services sit on the token generation path across every platform Claude runs on: every request must pass through them, and each millisecond of added latency is wait time for our users. You'll keep p99 latency flat as traffic grows, build for robustness as dependencies time out or partially fail, and ship changes safely to a system that cannot go down.
What you'll do:

• Design, build, and operate the backend systems that process every token on the generation path for Claude requests, including the streaming contract with the API and inference engines.
• Own latency and reliability end to end: define and maintain SLOs and error budgets for added latency, time-to-first-token, and availability, and lead incident response and postmortem follow-through.
• Ship changes to the hot path rapidly but safely - canaried and gradual rollouts, error budget and latency gating, fast rollbacks - and drive per-token performance: chase tail latency and keep cost flat as traffic, models, and checks per request grow.
• Set technical direction for the sampling path: lead design reviews, make latency, reliability, and cost trade-off calls with the inference and research teams, mentor engineers, and raise the operational bar for the wider Safeguards organization.

You may be a good fit if you:

• Have designed, built, and operated high QPS systems at global scale, and were accountable for them in production: incident response, outages, and postmortem-driven remediation.
• Have a strong foundation in distributed systems: replication, consistency tradeoffs, failure modes, and SLO management under load.
• Design systems for graceful degradation: you plan for a slow dependency, a dropped stream, or a half-rolled-out deploy before it happens, and build so the system degrades predictably instead of failing.
• Have successfully shipped broad or all-encompassing changes to mission critical systems (e.g., database migrations, interface changes, rewrites).

Strong candidates may also have:

• 8+ years of industry software engineering experience.
• Familiarity with LLM inference systems and transformer-based models (not required, but a plus).

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.

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