CUDA Kernel Performance Engineer

Acceler8 Talent; San Francisco (CA); Presencial; USD 150.000 - 190.000; Jornada completa

Descripción de la vacante

Acceler8 Talent in the San Francisco Bay Area is seeking a Kernel Engineer to advance accelerator performance on NVIDIA GPUs using CUDA. You will own low-level kernels, profile workloads, and push memory and compute balance for real-world autonomous systems.

You will explain bottlenecks, implement variants, and collaborate with neighboring teams to apply rapid, measurable improvements. A hands-on, problem-solving mindset and deep hardware intuition are essential.

Formación

• Hands-on authorship or substantial optimization of accelerator kernels.
• Strong computer architecture fundamentals and detailed reasoning about memory hierarchy and execution behavior.
• Ability to explain your own code-level changes and support performance claims with credible measurements.
• Independent problem solving, strong debugging skills and a preference for hands-on engineering.
• Ability to connect an observed performance problem to a hardware constraint and explain why a particular implementation change addresses it.
• Practical understanding of bandwidth, execution scheduling, memory hierarchy and register use, supported by personal engineering examples.
• A disciplined approach to measurement and debugging, including investigating new bottlenecks after an optimization.

Responsabilidades

• Author or materially optimize accelerator kernels, using the current NVIDIA and CUDA stack.
• Profile workloads and identify bottlenecks in memory access, execution, bandwidth and scheduling.
• Tune data movement, register use and parallel execution to make better use of accelerator resources.
• Validate correctness and quantify performance improvements under representative conditions.
• Investigate how bottlenecks change after an optimization and use the results to guide further work.
• Collaborate with adjacent engineering teams to apply low-level improvements within larger compute workloads.
• Establish a representative performance baseline and use profiling to distinguish compute limits from memory-bandwidth, scheduling or synchronization constraints.
• Examine memory layout, access patterns, register pressure and work distribution to identify the most effective low-level change.
• Implement and compare kernel variants, explaining the trade-offs between data movement, parallelism and hardware resource use.
• Use appropriate reference results and representative inputs to check numerical behavior and maintain correctness while improving performance.
• Communicate both the measured improvement and its operating conditions so other engineers can integrate the work effectively.

Conocimientos

Kernel optimization

CUDA profiling

Memory hierarchy

Performance measurements

Low-level debugging

Herramientas

CUDA

Descripción del empleo

Acceler8 Talent in the San Francisco Bay Area is seeking a Kernel Engineer to advance accelerator performance on NVIDIA GPUs using CUDA. You will own low-level kernels, profile workloads, and push memory and compute balance for real-world autonomous systems.

You will explain bottlenecks, implement variants, and collaborate with neighboring teams to apply rapid, measurable improvements. A hands-on, problem-solving mindset and deep hardware intuition are essential.