GPU Kernel Optimization Engineer

Acceler8 Talent; Palo Alto (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Acceler8 Talent is seeking a GPU Kernel Performance Engineer in Palo Alto to optimize kernels, analyze memory hierarchies, and drive system-level efficiency for AI workloads. You will own kernel development, profile bottlenecks, and work with ML infra teams to translate kernel gains into broader performance improvements.

The role is highly hands-on, with opportunities to shape GPU performance across architectures and accelerator stacks in a fast-moving startup environment.

Qualifications

• Hands-on experience developing, modifying, or optimizing GPU kernels using CUDA or comparable GPU/HPC programming technologies.
• Strong understanding of GPU architecture, including memory hierarchies, parallel execution, synchronization, and hardware utilization.
• Demonstrated ability to profile GPU workloads and identify underlying performance bottlenecks.
• Experience implementing performance improvements and measuring their impact using benchmarks or profiling tools.
• Ability to explain the relationship between GPU hardware constraints, software implementation choices, and measured performance.
• Strong programming fundamentals and experience debugging low-level performance issues.
• Willingness to work across GPU kernels, compiler/runtime behavior, and broader software systems.
• Strong technical problem-solving skills and the ability to work effectively in a fast-moving engineering environment.

Responsibilities

• Profile GPU workloads to identify performance bottlenecks, hardware limitations, and opportunities for improved efficiency.
• Optimize existing GPU kernels and develop new kernels where necessary to improve workload performance.
• Analyze GPU architectural constraints, including memory hierarchy, parallel execution, synchronization, and compute utilization.
• Investigate and optimize memory access patterns, kernel fusion, occupancy, and data movement.
• Develop reproducible performance benchmarks and measure improvements across representative AI workloads.
• Validate numerical correctness and performance consistency following kernel optimizations.
• Collaborate with ML infrastructure, serving, and applied machine learning teams to translate kernel-level improvements into system-wide performance gains.
• Investigate performance interactions across GPU kernels, compilers, runtimes, and supporting software infrastructure.
• Evaluate communication and memory bandwidth trade-offs across GPU and multi-GPU systems.

Skills

GPU kernel optimization

CUDA

GPU architecture

Profiling workloads

Performance tuning

Low-level debugging

System-level optimization

Programming fundamentals

Tools

CUDA toolkit

NVIDIA Nsight

Triton

CUTLASS

cuDNN

NVCC

PTX / SASS

Job description

Acceler8 Talent is seeking a GPU Kernel Performance Engineer in Palo Alto to optimize kernels, analyze memory hierarchies, and drive system-level efficiency for AI workloads. You will own kernel development, profile bottlenecks, and work with ML infra teams to translate kernel gains into broader performance improvements.

The role is highly hands-on, with opportunities to shape GPU performance across architectures and accelerator stacks in a fast-moving startup environment.