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.
At Anthropic, we are building some of the most complex and large-scale AI infrastructure in the world. As that infrastructure scales rapidly, so does the imperative to optimise how we use it. As a Software Engineer for Compute Efficiency on the Capacity team, you will play a central role in making our systems more performant, cost-effective, and sustainable—without compromising reliability or latency.
You will work across the full infrastructure stack, from cloud platforms and networking to application-level performance, and will bridge the gap between high-level research needs and low-level hardware constraints to build the most efficient AI infrastructure in the world. You will help with building the telemetry, cost attribution, and optimisation frameworks that ensure every dollar of our infrastructure investment delivers maximum value. This is a high-impact, cross-functional role at the intersection of systems engineering, financial optimisation, and AI infrastructure.
Responsibilities
- Build and evolve telemetry and monitoring systems to provide deep visibility into infrastructure performance, utilisation, and costs across our cloud and datacentre fleets.
- Design and implement cost attribution frameworks for our multi-tenant infrastructure, enabling teams to understand and optimise their resource consumption.
- Identify and resolve performance bottlenecks and capacity hotspots through deep analysis of distributed systems at scale.
- Partner closely with cloud service providers and internal stakeholders to optimise cluster configurations, workload placement, and resource utilisation across AI training and inference workloads—including large-scale clusters spanning thousands to hundreds of thousands of machines.
- Develop and champion engineering practices around efficiency, driving a culture of performance awareness and cost-conscious design across Anthropic.
- Collaborate with research and product teams to deeply understand their infrastructure needs, and design solutions that balance performance with cost efficiency.
- Drive architectural improvements and code-level optimisations across multiple services and platforms to deliver measurable utilisation and performance gains.
You may be a good fit if you:
- Have 6+ years of relevant industry experience, 1+ year leading large scale, complex projects or teams as a software engineer or tech lead
- Deep expertise in distributed systems at scale, with a strong focus on infrastructure reliability, scalability, and continuous improvement.
- Strong proficiency in at least one programming language (e.g., Python, Rust, Go, Java)
- Hands-on experience with cloud infrastructure, including Kubernetes, Infrastructure as Code, and major cloud providers such as AWS or GCP.
- Experience optimising end-to-end performance of distributed systems, including workload right-sizing and resource utilisation tuning.
- You possess a deep curiosity for how things work under the hood and have a proven ability to work independently to solve opaque performance issues
- Experience designing or working with performance and utilisation monitoring tools in large-scale, distributed environments.
- Strong problem-solving skills with the ability to work independently and navigate ambiguity.
- Excellent communication and collaboration skills—you will work closely with internal and external stakeholders to build consensus and drive projects forward.
Strong candidates may have:
- Experience with machine learning infrastructure workloads as well as associated networking technologies like NCCL.
- Low level systems experience, for example linux kernel tuning and eBPF
- Quickly understanding systems design tradeoffs, keeping track of rapidly evolving software systems
- Published work in performance optimisation and scaling distributed systems
Logistics
Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. 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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