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Staff+ Software Engineer, ML Sampling Path

Label Your Data·$320,000 – $485,000

Job description

About the Position

Staff+ Software Engineer, ML Sampling Path

San Francisco, CA

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

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.

Responsibilities

  • 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.

Requirements

  • 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).

Nice to Have

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

Compensation

The annual compensation range for this role is $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.

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.

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.

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.

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Label Your Data provides secure and high quality data annotation services for Computer Vision or NLP applications since 2020. We help Data Scientists and AI Engineers streamline the dataset labeling and focus on model development.About Label Your Data:— Founded in 2020;— 200+ annotation experts in Europe, LATAM and Africa;— 98%+ annotation accuracy benchmark;— 55 supported languages;— PCI/DSS certified;— ISO/IEC 27001:2013 certified;— GDPR, CCPA and HIPAA-compliant;— HQs in North America, EU and Asia.Our experience spans over 20 industries including Automotive, Robotics, Agriculture, E-commerce, Retail, Healthcare, Manufacturing, Fintech and Insurance.Our core services include but are not limited to:— Image & video annotation: 2D boxes, OCR, object/action detection, polygons, key points, semantic segmentation, 3D cuboids;— Text annotation: classification, NER, intent & sentiment analysis, prompts creation, Q&A pairs generation;— Audio annotation: transcription, sentiment recognition;— Sensor data annotation;— Data classification & categorization;— Data entry;— Data collection;— Model validation.

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