6+ years in technical program management or infrastructure operations in cloud environments
Direct experience managing GPU or accelerator fleets for training and inference
Proven track record with cloud providers on capacity commitments and vendor management
Strong background in infrastructure cost modeling and financial collaboration
Technical expertise in cluster management and data analysis for forecasting
Ability to communicate complex trade-offs clearly to diverse stakeholders
Comfortable in a small, agile team environment with a proactive approach
Responsibilities
Collaborate with Finance to forecast compute demand based on customer growth and product launches
Translate forecasts into actionable decisions regarding budget and capacity commitments
Lead the onboarding and transition of new capacity from contract to production
Create a comprehensive view of fleet performance, including cost and reliability metrics
Enhance daily utilization and cost-efficiency while managing idle capacity
Oversee end-to-end management of GPU and compute requests, streamlining processes
Maintain relationships with compute providers through performance reviews and scorecards
Work with cross-functional teams to align capacity decisions with various workload needs
Develop processes and tools for efficient compute request and allocation management
Benefits
Flexible work environment
Opportunities for professional growth and development
Collaborative team culture
Access to cutting-edge technology and resources
Health and wellness programs
Full Job Description
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.
What You'll Do
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
Qualifications
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
