Staff+ Software Engineer, ML Sampling Path

Anthropic; San Francisco (CA); Hybrid; USD 320,000 - 485,000; Full time

Benefits offered by this job

Competitive compensation

Equity donation matching

Generous vacation and parental leave

Flexible working hours

Office in San Francisco

Job summary

Anthropic is hiring for a Safeguards ML Sampling Path engineering role in San Francisco. You will design and operate backend systems for Claude, owning latency, reliability, and cost on the generation path.

You will lead design reviews, mentor engineers, and drive safe, incremental changes to a critical production system. The role requires strong distributed systems expertise and a track record of shipping major production changes.

Qualifications

• Have designed, built, and operated high QPS systems at global scale.
• Strong foundation in distributed systems: replication, consistency tradeoffs, failure modes, and SLO management under load.
• Design systems for graceful degradation to preserve availability during partial failures.
• Have shipped mission-critical system changes (e.g., migrations, major rewrites).
• 8+ years of industry software engineering experience.
• Familiarity with LLM inference systems and transformer-based models is a plus.

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.

Skills

High QPS systems

Distributed systems

Latency optimization

SLO management

Mentor engineers

Education

Bachelor's degree or equivalent

Tools

LLM inference systems

Transformer-based models

Job description

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 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.

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).

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

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.

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.