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
- As a member of the Compute team, you will play a critical role in Anthropic's mission of building safe and beneficial AI by ensuring we understand, optimize, and strategically manage our cloud infrastructure spend. Your work will directly impact how efficiently we operate our multi-cloud and datacenter footprint, from forecasting infrastructure needs and planning capacity, to driving utilization improvements and reducing unit costs across our compute, storage, and networking resources.
- You will work closely with Compute Finance, Infrastructure Engineers, and Product to translate raw cloud billing data into actionable efficiency insights and influence capacity planning & allocation. You will help build deep visibility into our infrastructure spend, forecast capacity needs, attribute costs accurately across teams and workloads, model resource demand curves, and help identify efficiency opportunities across our fleet. You've worked in cultures of excellence and are eager to bring analytical rigor and operational impact to a company scaling its infrastructure at an extraordinary pace.
Responsibilities
- Build and maintain cloud cost attribution models that accurately allocate infrastructure spend (compute, accelerators, storage, networking, data transfer) across teams, products, and workloads, providing clear visibility into who is spending what and why.
- Build and maintain cost of revenue pipelines and models
- Partner with infrastructure, finance, and procurement stakeholders to analyze utilization patterns, identify inefficiencies, and drive optimization initiatives that improve the cost-effectiveness of our non-accelerator cloud resources.
- Develop forecasting models for non-accelerator infrastructure demand, incorporating business growth projections, product roadmaps, and historical spend trends to enable proactive capacity planning and budget accuracy.
- Define and track unit cost metrics (e.g., cost per request, cost per GB stored, cost per pipeline run) and identify opportunities to reduce them, influencing infrastructure and engineering roadmaps with data-driven recommendations.
- Develop unit cost economics for various workloads and applications, and using the metrics to drive efficiency efforts across product and infrastructure teams.
- Build a cost-aware culture across the organization by creating self-serve dashboards, automated reporting, and accessible datasets that give engineering and finance teams clear visibility into cloud spend and efficiency metrics.
- Build and maintain cloud cost attribution models that accurately allocate infrastructure spend (compute, accelerators, storage, networking, data transfer) across teams, products, and workloads, providing clear visibility into who is spending what and why.
- Build and maintain cost of revenue pipelines and models
- Partner with infrastructure, finance, and procurement stakeholders to analyze utilization patterns, identify inefficiencies, and drive optimization initiatives that improve the cost-effectiveness of our non-accelerator cloud resources.
- Develop forecasting models for non-accelerator infrastructure demand, incorporating business growth projections, product roadmaps, and historical spend trends to enable proactive capacity planning and budget accuracy.
- Define and track unit cost metrics (e.g., cost per request, cost per GB stored, cost per pipeline run) and identify opportunities to reduce them, influencing infrastructure and engineering roadmaps with data-driven recommendations.
- Develop unit cost economics for various workloads and applications, and using the metrics to drive efficiency efforts across product and infrastructure teams.
- Build a cost-aware culture across the organization by creating self-serve dashboards, automated reporting, and accessible datasets that give engineering and finance teams clear visibility into cloud spend and efficiency metrics.
You might be a good fit if you have
- 6+ years of experience in data science, analytics, or FinOps roles, with a focus on cloud infrastructure cost analysis, capacity planning, or efficiency optimization.
- Experience building spend forecasting models and large-scale cost attribution systems.
- Deep knowledge of cloud billing systems, cost allocation methodologies, and spend optimization levers (e.g., reserved instances, committed use discounts, rightsizing, spot/preemptible usage).
- A passion for the company's mission of building helpful, honest, and harmless AI.
- Expertise in Python, SQL, forecasting, data modeling and data visualization tools.
- A bias for action and urgency, not letting perfect be the enemy of the effective.
- A strong disposition to thrive in ambiguity, taking initiative to create clarity and forward progress.
- A deep curiosity and energy for pulling the thread on hard questions.
- Experience in turning open questions and data into concise and insightful analysis.
- Highly effective written communication and presentation skills.
- 6+ years of experience in data science, analytics, or FinOps roles, with a focus on cloud infrastructure cost analysis, capacity planning, or efficiency optimization.
- Experience building spend forecasting models and large-scale cost attribution systems.
- Deep knowledge of cloud billing systems, cost allocation methodologies, and spend optimization levers (e.g., reserved instances, committed use discounts, rightsizing, spot/preemptible usage).
- A passion for the company's mission of building helpful, honest, and harmless AI.
- Expertise in Python, SQL, forecasting, data modeling and data visualization tools.
- A bias for action and urgency, not letting perfect be the enemy of the effective.
- A strong disposition to thrive in ambiguity, taking initiative to create clarity and forward progress.
- A deep curiosity and energy for pulling the thread on hard questions.
- Experience in turning open questions and data into concise and insightful analysis.
- Highly effective written communication and presentation skills.
- 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.
