AI Infrastructure Operations, Demand Planning

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.

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.

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.

• Take the Demand Planning forecast plus direct input from research, pretraining, and inference planners, and convert it into concrete accelerator, interconnect, region, supporting-resource, and date requirements for each tranche. Represent those in sourcing negotiations and data center build reviews, including which contractual terms actually move delivery dates.
• Qualify tranches for deliverability before signature. The Capacity Planner signs fit-to-forecast; you sign whether the shape can land schedulable, healthy, and instrumented in that region on that date, with storage, egress, identity in place.
• Close the delivery loop. Track forecast-versus-delivered on shape, region, and timing for every tranche; publish the variance; and feed it back to Demand Planning and into the next contract.
• Own the bring-up system of record. Define the canonical contract-to-occupied state machine with explicit entry and exit criteria per stage, and make it a first-class object in the capacity data layer so every downstream tool sees in-flight capacity, not only what has landed.
• Run a portfolio of bring-ups in parallel — new cloud regions, on-prem sites, neocloud blocks — with one integrated schedule spanning provider milestones, cluster creation, network turn-up, storage readiness, health burn-in, and first-workload landing.
• Drive readiness automation: All capacity systems are fully integrated for all new capacity, from contracted through ingested, automated and scaled.
• Instrument and publish the numbers that matter — time-to-occupied and paid-idle dollars per tranche — with executive-level reporting on status, tradeoffs, and risk across the portfolio.
• Significant experience delivering large-scale infrastructure — cloud regions, accelerator clusters, HPC systems, or bare-metal fleets — at multi-region scale or ≥10k accelerators (or CPU/storage equivalent).
• Technical range from through cluster orchestration and node health, up to the telemetry and planning tables on top — enough to debug where they disagree rather than route it.
• SQL and enough Python to answer your own questions and build your own reporting.
• A degree in a technical field or an equivalent engineering track record.
• Reserved-capacity onboarding, private offers, or capacity commitments with cloud or neocloud providers.
• Enough demand-planning exposure to challenge a forecast, translate it into per-tranche requirements, and feed delivery variance back into it.
• Data center or colocation delivery: power and space planning, network turn-up, site acceptance, vendor management.
• Accelerator health and burn-in, collective-communications sanity testing, or fleet-health SLOs — and a rigorous definition of "healthy."
• Systems of record or lifecycle services for infrastructure assets.
• Onboarding a new hardware generation into an existing scheduler and observability stack.

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.

Annual Salary:

$320,000—$405,000 USD

Logistics