Infrastructure Capacity Planner, Demand Planning

2 нед. назад

USA

capacity planningdemand forecastingresource management

Manage medium-range demand forecasts for infrastructure resources to support sourcing, purchasing, and optimization.

Обязанности

's infrastructure fleet spans a growing set of clouds, neoclouds, and on-prem sites.  Every part of it is expanding rapidly. Capacity Engineering is the team that connects every stage of that growth, from capacity planning to supply management to capacity delivery to utilization. We partner on supply deals, wire telemetry from day zero, own the canonical capacity data layer, and build the planning and enforcement tools every research and product team relies on.
As an Infrastructure Capacity Planner on the Demand Planning team, you'll own the medium-range demand forecast for every resource class we consume: accelerators, CPU, storage, network, and managed services. Your forecast will support sourcing and purchasing, prioritization, efficiency targets, and workload optimization.
Build and own the medium-range multi-resource demand forecast: accelerators by chip/interconnect class, CPU by shape, storage by tier and access pattern, egress by path, managed services by SKU — driven by model roadmap, RL/inference growth, eval volume, and retention policy rather than trend lines.
Run the plan-vs-reality loop. Diff planned allocations against observed fleet occupancy weekly, surface unrecorded trades and stale allocations, and drive variance toward zero with our planning-tools and data teams.
Qualify each incoming capacity tranche against the forecast before signature: right shape, region, quarter, and supporting-resource envelope (storage, egress, CPU). 
Partner with Finance and cost-efficiency teams to turn the forecast into core drivers covering the large majority (≥80%) of non-accelerator spend, and to aim savings work where waste will appear next.

Требования

Have done capacity, demand, or supply planning for large-scale technical infrastructure (cloud, HPC, hyperscale, or a large internal platform) and can articulate the difference between a forecast, a plan, and an allocation.
Build the model yourself rather than specifying it for someone else: SQL against a warehouse, Python/pandas or a proper forecasting/optimization stack.
Understand data-center resource classes well to know why storage and egress don't forecast like GPUs, and why a contract's headline chip count is rarely the binding constraint.
Prefer simple, inspectable models to clever opaque ones, and instrument your own forecast error.

Будет плюсом

Direct experience with cloud or neocloud providers on reserved-capacity onboarding, private offers, or capacity commitments.
Demand planning or forecasting experience for accelerator fleets, including translating research or product roadmaps into resource requirements.
Data center or colocation delivery experience: power and space planning, network turn-up, site acceptance criteria, and vendor management.
Experience with accelerator health and burn-in, collective-communications sanity testing, or fleet-health SLOs, and the ability to define healthy rigorously.
Experience building lifecycle or state-machine services, or systems of record for infrastructure assets.
Experience 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.

Другое

’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.
Minimum education: 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 recruiters only contact you
We believe that the highest-impact AI research will be big science. At 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 , including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
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.