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.
Our Inference team is responsible for building and maintaining the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry’s largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators.
The team has a dual mandate: maximizing compute efficiency to reliably serve our explosive customer growth, while enabling breakthrough research by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. We tackle complex, distributed systems challenges across multiple accelerator families and emerging AI hardware running in multiple cloud platforms.
Inference systems are highly performance sensitive distributed systems. Inference serves hundreds of thousands of customers every day, and the size & span of the inference fleet requires sophisticated routing, scaling, and networking systems.
Key responsibilities- Design, build, and maintain the distributed systems that serve Claude to millions of users worldwide
- Develop resilient, flexible systems that adapt in real time to real world events
- Develop intelligent request routing, load balancing, and traffic management systems across thousands of accelerators
- Maximize compute efficiency across the fleet by autoscaling and orchestrating production, research, and experimental workloads
- Build and operate production-grade deployment pipelines for releasing new models to users
- Provide high-performance inference infrastructure that enables researchers to develop next-generation models
- Integrate new AI accelerator platforms and support inference for new model architectures
- Significant software engineering experience, particularly with distributed systems
- Results-oriented, with a bias towards flexibility and impact
- Willingness to pick up slack, even if it goes outside your job description
- Desire to learn more about machine learning systems and infrastructure
- Thrive in environments where technical excellence directly drives both business results and research breakthroughs
- Care about the societal impacts of your work
- Experience with high-performance, large-scale distributed systems
- Experience implementing and deploying machine learning systems at scale
- Experience with load balancing, request routing, or traffic management systems
- Familiarity with LLM inference optimization, batching, and caching strategies
- Experience with Kubernetes and cloud infrastructure (AWS, GCP, Azure)
- Proficiency in Python or Rust
- Designing intelligent routing algorithms that optimize request distribution across many accelerators in different environments
- Autoscaling our compute fleet to dynamically match supply with demand across production, research, and experimental workloads
- Building production-grade deployment pipelines for releasing new models to millions of users reliably
- Contributing to new inference features
- Supporting inference for new model architectures
- Analyzing observability data to tune performance based on real-world production workloads
- Managing multi-region deployments and geographic routing for global customers
Deadline to apply: None. Applications will be reviewed on a rolling basis.
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.
Skills Required
- Strong software engineering skills, including designing systems that manage complex state machines and multi-stage pipelines
- Proficiency with Kubernetes-based deployments, rolling update mechanics, and container orchestration
- Experience building deployment, release, or delivery infrastructure shaped by resource constraints (fleet capacity, network bandwidth, hardware availability)
- Track record of building automation that measurably improves deployment velocity and reliability
- Comfort working across the stack: backend services, databases, CLI tools, and web UIs
- Strong communication skills and ability to work closely with oncall engineers, model teams, and infrastructure partners
- Minimum education: Bachelor's degree or equivalent combination of education, training, and/or experience
- 5+ years building deployment, release, or delivery infrastructure at scale
- Experience with Python and/or Rust in production systems
- Experience with ML inference or training infrastructure deployment across multiple accelerator types (GPU, TPU, Trainium)
- Background in capacity planning or resource-constrained scheduling (bin-packing, fleet management, hardware affinity)
- Experience with progressive delivery for long validation cycles: canary/soak testing, blue-green deployments, traffic shifting, automated rollback
- Experience at companies with large-scale release engineering challenges (monorepo deployments, multi-datacenter rollouts, mobile release trains)
Anthropic Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Anthropic and has not been reviewed or approved by Anthropic.
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Strong & Reliable Incentives — Pay is positioned as top-of-market for many technical roles through a mix of high base pay, equity, and occasional bonuses/signing incentives. Benefits like substantial monthly stipends and employer-paid protections further strengthen perceived total rewards.
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Healthcare Strength — Healthcare is described as comprehensive across medical, dental, and vision, with additional mental-health support. Coverage is framed as robust for employees and dependents, which can materially increase the value of the overall package.
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Parental & Family Support — Paid parental leave is described as notably generous, alongside fertility coverage and other family-oriented supports. These elements broaden the rewards package beyond cash compensation and can improve retention for caregivers.
Anthropic Insights
What We Do
Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.
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