GPU/Kernel Engineer

Acceler8; 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

GPU / Kernel Performance Engineer

Location:Mountain View, CA

Work Arrangement:Onsite

About the Role

An innovative AI infrastructure startup is seeking a GPU / Kernel Performance Engineer to optimize the performance, power efficiency, and cost of large-scale AI training and inference workloads.

The company is developing intelligent software that analyzes AI workloads and improves how they execute on modern accelerator hardware.

This is a highly hands-on engineering role offering direct ownership of GPU performance optimization, kernel development, and system-level efficiency improvements. You'll work closely with a small, highly technical team spanning GPU computing, ML infrastructure, and performance engineering.

The team is open to both dedicated GPU kernel specialists and broader performance engineers who have experience modifying and optimizing low-level GPU code.

Key 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.

Required 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.

Preferred Qualifications

• Experience with GPU programming frameworks and libraries such as CUDA, Triton, CUTLASS, or cuDNN.
• Familiarity with low-level GPU instructions and optimization using PTX or SASS.
• Experience with GPU profiling tools such as NVIDIA Nsight Systems or Nsight Compute.
• Knowledge of roofline analysis, Tensor Cores, warp specialization, memory coalescing, and occupancy optimization.
• Understanding of GPU memory systems, including HBM, shared memory, caches, and memory bandwidth limitations.
• Experience with multi-GPU communication technologies such as NCCL, NVLink, or NVSwitch.
• Familiarity with AMD GPU programming using ROCm/HIP.
• Experience with TPU/XLA or performance optimization across multiple accelerator architectures.
• Background optimizing AI training, inference, or high-performance computing workloads.

Why Join?

• Work on core performance optimization technology for next-generation AI infrastructure.
• Take direct ownership of GPU kernel and system-level performance improvements.
• Collaborate with experienced engineers from leading AI hardware and accelerator development teams.
• Gain exposure to GPU architecture, compiler/runtime behavior, and large-scale AI infrastructure.
• Work within a small engineering team where individual contributions have a direct impact on product performance.
• Opportunity for exceptional graduates and early-career engineers to take on challenging, high-impact technical work.