Software Engineer - GPU Kernels
Remote • San Francisco • FullTime
Posted 1y ago
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
Applied AI Engineer
About the job
Baseten is seeking a GPU Kernel Engineer to join our team at the forefront of AI acceleration. In this role, you will craft the foundational code that powers modern AI workloads, optimizing every microsecond of computation to enable breakthrough applications. You will work in a fast-paced, intellectually stimulating environment where technical excellence is paramount and your contributions directly influence production systems serving millions of users across numerous products. This role offers exceptional growth potential for engineers passionate about low-level optimization and high-impact systems work.
Responsibilities
- Design and implement high-performance GPU kernels for key ML operations like matrix multiplications, attention mechanisms, and mixture-of-experts routing.
- Write and optimize code using CUDA, PTX assembly, and architecture-specific techniques.
- Apply advanced performance optimization methods such as memory coalescing, warp-level programming, tensor core acceleration, and compute/memory overlap.
- Implement cutting-edge features like quantization (FP8/FP4), sparsity, and compute/communication overlap.
- Identify and resolve performance bottlenecks using tools like Nsight Systems, Nsight Compute, and Torch Profiler.
- Collaborate with research teams to productionize theoretical advancements.
- Contribute to internal and open-source GPU libraries.
- Present technical contributions at industry conferences (e.g., NVIDIA GTC, AWS re:Invent).
Requirements
- Strong understanding of GPU architecture and programming paradigms, including memory hierarchy, thread/block/grid organization, and synchronization techniques.
- Proficiency in C++ and GPU performance profiling tools.
- Knowledge of CUDA C++ API, memory access patterns and bandwidth optimization, numerical precision and quantization strategies, and modern GPU features.
- Experience with Transformer models and attention optimization (e.g., Flash Attention).
- Familiarity with GPU kernel libraries like Cutlass, Triton, Thrust, CUB.
- Background in GEMM tuning and distributed/multi-GPU compute.
- Contributions to open-source GPU projects.
- Research publications or conference presentations on GPU performance.
Benefits
- Competitive compensation, including meaningful equity.
- 100% coverage of medical, dental, and vision insurance for employee and dependents.
- Flexible PTO policy including company wide Winter Break.
- Paid parental leave.
- Fertility and family-building stipend.
- Company-facilitated 401(k).
- Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.