Senior Manager, Capacity and Performance Engineering

General Motors•Sunnyvale, CA

1d•Hybrid

About The Position

GM is building a next-generation autonomous vehicle system targeting highway launch. That system runs on GPU clusters for model training, on large-scale simulation infrastructure for validation, and on data pipelines that ingest and process sensor data at petabyte scale. All of it cloud-hosted, all of it needs to scale predictably as the program accelerates. As Senior Manager of AV Cloud Capacity & Performance Engineering, you own the team and function responsible for making sure that infrastructure is always scaled, efficiently utilized, and fiscally sound. You'll build and operate the capacity modeling, cost optimization, performance benchmarking, and vendor management capabilities that allow the AV engineering organization to develop without resource ceilings. In practice, this means making calls like: committing to a GPU procurement months before demand materializes based on program signals, calling a cross-team efficiency initiative when utilization data reveals a pattern of waste, or recommending to VP leadership that a next-generation hardware cluster is worth the migration cost based on your team's benchmark data.

Requirements

Experience leading technical teams: You've led engineers and/or technical program managers in a cloud infrastructure, capacity planning, or ML/AI systems function. You've been accountable for the team's outcomes, invested in its members' development, and built a culture where precision and proactive problem-finding are norms.
Deep fluency in GPU compute and cloud economics: You understand the tradeoffs between reserved and on-demand cloud capacity, how GPU cluster utilization is measured and improved, and how to build a multi-year infrastructure cost model for a large AI or ML program. You can hold your own in a detailed technical conversation with a Staff engineer and a finance partner in the same week.
Experience developing capacity planning as an organizational capability: You've built the models, processes, and reporting frameworks an engineering organization relies on for infrastructure forecasting and not just executed within a system someone else built. You've presented capacity plans and their assumptions to VP-level audiences and been accountable for the outcomes.
Track record on infrastructure efficiency or cost programs: You've owned programs that produced measurable, auditable savings with before/after data, clear attribution, and closed loops. You can describe the gap you identified, the intervention your team drove, and the result.
Cross-functional influence without authority: You've driven alignment across engineering, finance, procurement, and program management where stakeholders had competing priorities. You know how to frame tradeoffs for senior audiences and navigate to a decision.
Clear written and verbal communication at executive levels: You've written strategy documents, capacity plans, and budget briefings for VP-level audiences. You know when to lead with the recommendation and when to walk through the evidence.
5+ years of experience or proven record of defining and executing technical strategy that required coordination across multiple teams, senior executives, and front-line engineers
Bachelors Degree in Computer Science or related field OR equivalent work experience
Experience driving consensus on a multi-functional effort using influence without authority
Experience in designing and implementing large scale systems
Experience with infrastructure for large-scale AI or ML programs: GPU training clusters, ML pipeline orchestration, or inference serving at production scale.
Background in performance engineering or HPC: workload characterization, cluster benchmarking, GPU efficiency modeling.
Familiarity with complex engineering programs with fixed timelines (automotive, aerospace, or similar) where infrastructure availability has direct program milestone consequences.
Experience managing enterprise cloud or hardware vendor relationships at a program level, including capacity commitments and multi-year procurement.

Responsibilities

Own the compute capacity forecast the AV program depends on
Build and maintain detailed capacity models for the full AV compute stack: model training clusters, simulation infrastructure, data ingest systems, inference serving, and developer tooling.
Translate upcoming program milestones into specific capacity commitments well ahead of when supply is needed.
Own the GPU supply plan end-to-end: assessing when to transition workloads across hardware generations, aligning procurement timing with vendor product cycles, and ensuring supply commitments are in place before engineering demand materializes.
Run an infrastructure efficiency and cost optimization program with measurable targets
Lead a cross-team program to reduce compute and storage waste without degrading engineering velocity. Your team surfaces over-provisioned workloads, identifies GPU scheduling and utilization inefficiencies, and develops architectural recommendations that improve cost per experiment. You define the targets, own the results reporting to VP leadership, and drive execution in partnership with the engineering teams that own the workloads.
Operate the performance lab and deliver actionable benchmarks to engineering leadership
Lead the function that characterizes how AV workloads perform on the organization's infrastructure training throughput on current and next-generation GPU clusters, simulation efficiency per node-hour, and storage I/O characteristics.
Establish repeatable benchmarks that answer concrete procurement and architecture questions: Is the next GPU generation worth the migration cost? Where are the actual throughput bottlenecks for training workloads? Deliver findings as engineering-grade recommendations, not observational reports.
Own strategic vendor and cloud provider relationships
Own the working relationships with major cloud providers and hardware vendors at a program level.
Define and enforce SLOs for vendor responsiveness and supply commitments.
Build the monitoring and escalation infrastructure to catch vendor performance issues before they become engineering blockers.
Negotiate supply and pricing terms that serve a multi-year capacity roadmap.
Advise VP and Director-level leaders on infrastructure tradeoffs and program risk
Be the person senior engineering and program leaders turn to when they need to understand the infrastructure cost and timeline implications of a roadmap decision.
Translate complex capacity economics (GPU procurement lead times, cloud burst vs. reserved capacity tradeoffs, compute efficiency opportunities) into the language of program risk, engineering velocity, and budget impact.
Build the GPU strategy and determine plan to use next gen chip sets for better performance.
Build the team and define how the function operates
Lead a team of engineers and technical program managers working across capacity modeling, performance benchmarking, cost optimization, and vendor operations.
Invest in the development of everyone on the team. You care as much about how the team grows as what it delivers.
Establish the metrics, standards, and reporting practices that define how AV Infrastructure measures and manages compute efficiency. You set baselines against which engineering teams can be held accountable, and that give senior leadership the visibility to make confident tradeoff decisions.