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.
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.
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.
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.
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
Top Skills
Similar Jobs
What you need to know about the NYC Tech Scene
Key Facts About NYC Tech
- Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Capgemini, Bloomberg, IBM, Spotify
- Key Industries: Artificial intelligence, Fintech
- Funding Landscape: $25.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Greycroft, Thrive Capital, Union Square Ventures, FirstMark Capital, Tiger Global Management, Tribeca Venture Partners, Insight Partners, Two Sigma Ventures
- Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory
