Join Perplexity as a technical program manager to oversee GPU compute planning and operations for innovative products.
Posted by employer 4 days ago
First seen on Joblaze 2 days ago
Last verified on the company career page 11 hours ago
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Not disclosed in this posting: compensation, work arrangement, visa sponsorship.
Joblaze summary
In this role, the technical program manager at Perplexity is responsible for overseeing the planning, implementation, and operation of GPU compute resources that support various products. The position requires expertise in managing GPU fleets, capacity planning, and collaboration with finance and engineering teams to ensure efficient resource allocation and performance monitoring. Ideal candidates will have significant experience in technical program management within cloud environments and a strong understanding of infrastructure operations. The role suits individuals who thrive in agile settings and are comfortable navigating complex technical and commercial landscapes.
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From the original posting
Perplexity is looking for a technical program manager to own how we plan, bring online and run the GPU compute behind every product.
Perplexity serves Ask, Computer and API traffic on GPU capacity across multiple cloud and neocloud providers, and demand shifts with every new customer, product launch and model release. Working across engineering, research, Finance and providers, you’ll forecast how much compute we need and when, bring new capacity into production, and own how the fleet performs today, driving fixes when reliability, utilization or cost fall short. The ideal candidate knows GPU fleets, handles technical and commercial details, and is energized by building a function with little precedent.
Perplexity’s mission is to power curiosity. Curious people are the people who drive change in the world. Driving change is a continuous cycle of learning, building, and integrating.
Learn: curious people constantly learn new things by asking more. They question the status quo in their own expertise and they constantly learn outside of it. Research is essential to them and never ending.
Build: curious people make and create things, to show the world their new answers to problems no one else ever questioned. They take action on what they’ve learned. Makers need tools to create their products, their companies, their reality.
Integrate: they must interact with the world as it is to drive change and adoption. True leaders do not simply build something and hope. They must have armies of agents and workers who can constantly work in millions of small ways.
Partner with Finance on the compute demand forecast, 3–12 months out, driven by customer growth, model releases, and post-training and evaluation plans rather than trend lines
Turn the forecast into decisions with Finance and engineering leadership on commitments and budget, checking that new capacity fits the plan before we commit
Bring new capacity online and lead transitions, from contract to production, including provider onboarding, acceptance criteria, migration and decommissioning
Build a single, trusted view of the fleet: capacity, cost, allocation, and uptime, reliability and utilization by provider and workload
Drive daily utilization, reliability and cost-efficiency as core metrics, reclaiming idle capacity and surfacing trade-offs rather than letting them become side effects
Own inference and compute requests end to end: triage and resolve GPU, deployment and quota requests from teams across Perplexity, and build self-serve paths so routine requests no longer need a person in the loop
Manage compute provider relationships, with scorecards and regular reviews of reliability, support and delivery, and use that data to shape how we work with providers over time
Partner with engineering, research, and finance teams on capacity decisions that match the different needs of training, batch inference and real-time production inference
Build the operating system for compute: the processes and tooling to request, plan, allocate and track compute, with clear owners
6+ years of technical program management, capacity planning or infrastructure operations in cloud environments, leading programs across teams and vendors
Direct experience with GPU or accelerator fleets across training and inference workloads
Experience working directly with cloud or neocloud providers on reserved capacity, capacity commitments, onboarding or vendor performance management
Experience with infrastructure cost modeling, unit economics or cost attribution in partnership with Finance
Technical depth in cluster management, scheduling and orchestration, and the data skills to build forecasts and dashboards yourself
The judgment to surface hard trade-offs and communicate them clearly from engineers to executives
Thrives in a small, agile team; has initiative and desire for ownership without much precedent to lean on