Member of Technical Staff - GPU Performance

San Francisco, CAfull timeOn Site

Apply on Employer Site

senior

$150k–$390k

H1B: no

Gimlet Member of Technical Staff responsible for characterizing and improving GPU performance for AI inference across heterogeneous accelerator fleets. Focus on building measurement systems and performance methodologies to optimize end-to-end latency, throughput, and cost.

Responsibilities

As a Member of Technical Staff, you will characterize and improve the performance of production AI inference across Gimlet's heterogeneous accelerator fleet.
What success looks like
In the first 12-18 months, you will:
Establish trustworthy performance baselines for production inference workloads across established and emerging accelerator architectures
Isolate bottlenecks across compute, memory bandwidth, cache behavior, kernel launch, synchronization, host-device interaction, communication, and runtime scheduling ● Use profiling, hardware counters, controlled experiments, and performance models to explain why a workload behaves as it does and identify the highest-leverage optimization
Improve execution through changes such as launch reduction, graph capture, stream and event coordination, compute-communication overlap, memory reuse, batching, and scheduling ● Build automated benchmarks and regression detection that preserve performance across models, tensor shapes, data types, software releases, and hardware targets
Translate performance findings into production improvements, partnering with kernel, compiler, ML systems, and distributed systems engineers when necessary
Develop a repeatable methodology for evaluating and bringing up new accelerators within Gimlet's execution platform
You may be a good fit if you have
Strong software engineering fundamentals and experience working on performance-critical systems
Experience profiling GPU or accelerator workloads and turning measurements into production improvements
A strong understanding of GPU execution, memory hierarchies, asynchronous execution, synchronization, and performance tradeoffs

Qualification

Strong understanding of GPU execution

Required

Strong software engineering fundamentals
Experience working on performance-critical systems
Experience profiling GPU or accelerator workloads
Strong understanding of GPU execution, memory hierarchies, asynchronous execution, synchronization, and performance tradeoffs
Ability to design controlled experiments and communicate performance findings precisely
Bachelor's degree in a relevant field or equivalent combination of education and training

Company

Gimlet

View company page

Source: ashby

Active within the past hour

More roles at Gimlet

Executive Business Partner

Materials and Product Lifecycle Program Manager

San Francisco, CA

Boost Your Interview Chances

Improve Resume Match Score

Free

Your Score

9.0

Strong Applicant

Must-Have Skills for This Role

GPUprofilinghardware counterskernel executioncompilerruntime behavior

Optimize my Resume

Apply Faster with Autofill

Free

Save your profile once. LumiJobs pre-fills the employer's application form so you stop re-typing the same answers on every site.

Apply With Autofill

Or skip LumiJobs's tools and submit directly on the employer's career site.

Apply on Employer Site

Member of Technical Staff - GPU Performance at Gimlet · LumiJobs