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Performance engineer • berkeley ca
GPU Performance Engineer
GenmoSan Francisco, CA, USPerformance Engineer LMTS
Salesforce, Inc.San Francisco, CA, United StatesSoftware Engineer (AI Performance)
Gimlet Labs, IncSan Francisco, CA, United StatesStaff Infrastructure and Performance Engineer
NashSan Francisco, CA, United States- Promoted
Performance ML Engineer : CUDA, GPU Systems
RelaceSan Francisco, CA, United StatesPerformance Engineer LMTS
SalesforceSan Francisco, California, United StatesTechnical Support Engineer, Application Performance
CloudflareSan Francisco, California, USAGPU Systems Engineer : High-Performance C++
10X Recruiting PartnersSan Francisco, CA, United StatesGPU Performance Engineer
Genmo Inc.San Francisco, CA, United StatesSenior HPC Performance Engineer
NVIDIARemote, CA, USTraining Performance Engineer
OpenAISan FranciscoBuilding Performance Engineer
Harrison Consulting SolutionsSan Francisco, United StatesSeniior Staff Software Engineer - Performance Engineering
FivetranOakland, California, United StatesBackend Engineer - High-Performance Search Systems
ExaSan Francisco, CA, United StatesPerformance Engineer
Menlo VenturesSan Francisco, CA, United StatesHPC / AI Data Performance Engineer
Lawrence Berkeley National LaboratoryBerkeley, CA, United StatesSr. Software Engineer - Performance
DatabricksSan Francisco, CaliforniaFounding Resilience Engineer - Performance & Observability
PersonaSan Francisco, CA, United StatesThe average salary range is between $ 129,875 and $ 173,510 year , with the average salary hovering around $ 153,425 year .
- software development manager (from $ 220,000 to $ 273,000 year)
- nuclear medicine (from $ 153,470 to $ 250,984 year)
- veterinarian (from $ 115,000 to $ 250,000 year)
- python developer (from $ 135,000 to $ 244,125 year)
- office administrative assistant (from $ 47,840 to $ 243,900 year)
- vp of engineering (from $ 68,428 to $ 237,500 year)
- embedded systems engineer (from $ 133,875 to $ 222,134 year)
- product director (from $ 157,500 to $ 220,750 year)
- applications engineer (from $ 149,709 to $ 218,500 year)
- startup (from $ 136,250 to $ 216,250 year)
- Anaheim, CA (from $ 77,756 to $ 240,760 year)
- Durham, NC (from $ 107,250 to $ 233,490 year)
- Santa Clara, CA (from $ 140,000 to $ 214,000 year)
- San Mateo, CA (from $ 125,863 to $ 212,250 year)
- San Jose, CA (from $ 125,358 to $ 211,691 year)
- Baltimore, MD (from $ 145,000 to $ 210,100 year)
- Palm Bay, FL (from $ 149,550 to $ 208,490 year)
- Des Moines, IA (from $ 102,600 to $ 208,000 year)
- San Francisco, CA (from $ 121,800 to $ 205,272 year)
- Sunnyvale, CA (from $ 124,500 to $ 205,110 year)
The average salary range is between $ 106,478 and $ 185,900 year , with the average salary hovering around $ 130,004 year .
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GPU Performance Engineer
GenmoSan Francisco, CA, US- Full-time
Job Description
Job Description
We are Genmo, a research lab dedicated to building open, state-of-the-art models for video generation towards unlocking the right brain of AGI. Join us in shaping the future of AI and pushing the boundaries of what's possible in video generation.
We're seeking a GPU Performance Engineer to squeeze every last FLOP from our H100 infrastructure and optimize our model serving stack to its absolute limits.
The Role
You'll be our performance optimization expert, using advanced profiling tools to identify bottlenecks and implementing solutions that achieve 5-10x speedups. From writing custom CUDA kernels to eliminating cold start latency, you'll ensure our infrastructure delivers world-class performance. This role is perfect for someone who gets excited about microsecond optimizations and pushing hardware to its theoretical limits.
Key Responsibilities
Profile and optimize GPU workloads using Nsight Systems, nvprof, and custom instrumentation
Write high-performance CUDA and Triton kernels for critical model operations
Optimize cold start latency from seconds to milliseconds for our serving infrastructure
Tune memory access patterns, kernel fusion, and GPU utilization
Collaborate with ML engineers to optimize model implementations
Debug performance issues across the full stack from application to hardware
Implement custom memory pooling and allocation strategies
Share optimization techniques and build performance culture across teams
Qualifications
Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field
5+ years systems programming experience with 3+ years focused on GPU optimization
Expert proficiency with GPU profiling tools (Nsight Systems, nvprof)
Strong CUDA programming skills with production kernel development
Deep understanding of GPU architecture (memory hierarchy, SMs, warps)
Track record of achieving significant performance improvements (5-10x)
Experience with Python and C++ in production environments
We Value
Experience with Triton kernel development
Knowledge of CUTLASS or similar high-performance libraries
Background in ML-specific optimizations (attention, transformers)
RDMA / InfiniBand optimization experience
Contributions to GPU libraries or frameworks
Low-level debugging skills (PTX / SASS reading)
Genmo is an Equal Opportunity Employer. Candidates are evaluated without regard to age, race, color, religion, sex, disability, national origin, sexual orientation, veteran status, or any other characteristic protected by federal or state law. Genmo, Inc. is an E-Verify company and you may review the Notice of E-Verify Participation and the Right to Work posters in English and Spanish.