Software Engineer, Compute Efficiency
San Francisco, CA | New York City, NYFull-TimeMid-levelSoftware Engineering
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.
- 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 optimize 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 optimization 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 optimization, and AI infrastructure.
- Responsibilities:
- Build and evolve telemetry and monitoring systems to provide deep visibility into infrastructure performance, utilization, and costs across our cloud and datacenter fleets.
- Design and implement cost attribution frameworks for our multi-tenant infrastructure, enabling teams to understand and optimize 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 optimize cluster configurations, workload placement, and resource utilization 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 optimizations across multiple services and platforms to deliver measurable utilization and performance gains.
- Build and evolve telemetry and monitoring systems to provide deep visibility into infrastructure performance, utilization, and costs across our cloud and datacenter fleets.
- Design and implement cost attribution frameworks for our multi-tenant infrastructure, enabling teams to understand and optimize 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 optimize cluster configurations, workload placement, and resource utilization 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 optimizations across multiple services and platforms to deliver measurable utilization 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 optimizing end-to-end performance of distributed systems, including workload right-sizing and resource utilization 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 utilization 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.
- 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 optimizing end-to-end performance of distributed systems, including workload right-sizing and resource utilization 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 utilization 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 optimization and scaling distributed systems
- 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 optimization and scaling distributed systems
- 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.
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.
