GPU Kernel Performance Architect

Acceler8 Talent; California; On-site; USD 150,000 - 230,000; Full time

Job summary

Acceler8 Talent is seeking a GPU Kernel Performance Engineer in Mountain View, CA to optimize performance, power efficiency, and cost of large-scale AI workloads. You will own kernel development, profile workloads, and collaborate with ML infra teams to translate kernel gains into system-wide improvements.

Onsite role with hands-on optimization across GPUs and accelerators. This role offers exposure to GPU architecture, compiler/runtime behavior, and large-scale AI infrastructure within a small,

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 bottlenecks and opportunities for efficiency.
• Optimize existing GPU kernels and develop new kernels to improve workload performance.
• Analyze 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 AI workloads.
• Validate numerical correctness and performance consistency after kernel optimizations.
• Collaborate with ML infrastructure, serving, and ML teams to translate kernel gains to system-wide gains.
• Investigate performance interactions across GPU kernels, compilers, runtimes, and supporting software.
• Evaluate communication and memory bandwidth trade-offs across GPU and multi-GPU systems.

Skills

CUDA kernel optimization

GPU architecture knowledge

Workload profiling

Performance benchmarking

Low-level debugging

Parallel programming

System-level optimization

Tools

CUDA

Nsight Systems

Nsight Compute

ROCm/HIP

NCCL

Job description

Acceler8 Talent is seeking a GPU Kernel Performance Engineer in Mountain View, CA to optimize performance, power efficiency, and cost of large-scale AI workloads. You will own kernel development, profile workloads, and collaborate with ML infra teams to translate kernel gains into system-wide improvements.

Onsite role with hands-on optimization across GPUs and accelerators. This role offers exposure to GPU architecture, compiler/runtime behavior, and large-scale AI infrastructure within a small,