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Sciforium

GPU Kernel Engineer

San Francisco, CA · On-site

$190K - $250K/yr

About the role We are seeking a highly skilled GPU Kernel Engineer who is passionate about pushing the limits of performance on modern accelerators. In this role, you will design and optimize custom ...

Coda Robotics

GPU Kernel Engineer

San Francisco, CA · On-site

$100K - $120K/yr

Lead a team of kernel and system engineers focused on performance-critical code * Design, implement, and optimize custom compute kernels for CPU (AVX/ARM NEON), GPU (CUDA/ROCm), and hardware ...

Advanced Micro Devices

We are looking for a talented and motivated GPU kernel developer with broad and deep experience in compute and machine learning software development to deliver out of the box high performance ROCm ...

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As of Sep 10, 2026, the average hourly pay for gpu kernel programmer in the United States is $39.54, according to ZipRecruiter salary data. Most workers in this role earn between $25.72 and $51.44 per hour, depending on experience, location, and employer.

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Infographic showing various Gpu Kernel Programmer job openings in the United States as of August 2026, with employment types broken down into 96% Full Time, 1% Part Time, 1% Temporary, and 2% Contract. Highlights an 82% Physical, 6% Hybrid, and 12% Remote job distribution, with an average salary of $82,234 per year, or $39.5 per hour.

GPU Kernel Engineer

San Francisco, CA • On-site

Sciforium
See more jobs at Sciforiumexternal

$190K - $250K/yr

Full-time

Medical, Dental, Vision, Retirement

Re-posted 8 days ago

Job description

Sciforium is an AI infrastructure company developing next-generation multimodal AI models and a proprietary, high-efficiency serving platform. Backed by multi-million-dollar funding and direct sponsorship from AMD with hands-on support from AMD engineers the team is scaling rapidly to build the full stack powering frontier AI models and real-time applications.
About the role
We are seeking a highly skilled GPU Kernel Engineer who is passionate about pushing the limits of performance on modern accelerators. In this role, you will design and optimize custom GPU kernels that power next-generation large-scale AI systems. You will work across the hardware-software stack, from low-level kernel development to integrating optimized ops into high-level ML frameworks used for large-scale training and inference.
This role is ideal for someone who thrives at the intersection of GPU programming, systems engineering, and cutting-edge AI workloads, and who wants to make meaningful contributions to the efficiency and scalability of our ML platform.
Key Responsibilities
  • Design, implement, and optimize custom GPU kernels using C++, PTX, CUDA, ROCm, Triton, and/or JAX Pallas.
  • Profile and optimize end-to-end performance of ML operations, with a focus on large-scale LLM training and inference.
  • Integrate low-level GPU kernels into frameworks such as PyTorch, JAX, and custom internal runtimes.
  • Develop performance models, identify bottlenecks, and deliver kernel-level improvements that significantly accelerate AI workloads.
  • Collaborate with ML researchers, distributed systems engineers, and model-serving teams to optimize compute performance across the stack.
  • Work closely with hardware vendors (NVIDIA/AMD) and stay current on the latest GPU architecture capabilities and compiler/toolchain improvements.
  • Contribute to tooling, documentation, benchmarking suites, and testing frameworks to ensure correctness and performance reproducibility.

Must-Haves
  • 5+ years of industry or research experience in GPU kernel development or high-performance computing.
  • Bachelor's, Master's, or PhD in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or a related field.
  • Strong programming skills in C++, Python, and familiarity with ML frameworks.
  • Deep expertise in CUDA/ROCm, GPU memory models, and performance optimization strategies.
  • Hands-on experience with Triton and/or JAX Pallas for custom kernel development.
  • Strong understanding of PTX, GPU ASM, and low-level GPU execution.
  • Extensive experience writing and optimizing custom GPU kernels in C++ and PTX.
  • Proven ability to integrate low-level kernels into PyTorch, JAX, or similar frameworks.
  • Experience working with large-scale LLM workloads (training or inference).

Nice-to-Haves
  • Experience with AMD GPUs and ROCm optimization.
  • Familiarity with JAX FFI and custom ML operator development.
  • Experience with efficient model serving frameworks (e.g., vLLM, TensorRT).
  • Experience with TPUs, XLA, or similar accelerator programming environments.
  • Contributions to open-source ML systems, compilers, or GPU kernels.

Benefits include
  • Medical, dental, and vision insurance
  • 401k plan
  • Daily lunch, snacks, and beverages
  • Flexible time off
  • Competitive salary and equity

Equal opportunity
Sciforium is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

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