Software Engineer - GPU Kernels

We’re seeking a GPU Kernel Engineer to join our team at the cutting edge of AI acceleration, where your code directly impacts the performance of state-of-the-art machine learning models. As a GPU Kernel Engineer, you'll craft the foundation that powers modern AI workloads, optimizing every microsecond of computation to enable breakthrough applications.

You'll 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.

EXAMPLE INITIATIVES

You'll get to work on these types of projects as part of our Model Performance team:

• Baseten Embeddings Inference: The fastest embeddings solution available

• The Baseten Inference Stack

• Driving model performance optimization

Core Engineering Responsibilities

• Design and implement high-performance GPU kernels for key ML operations, including 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

Performance & Innovation

• 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

Impact & Collaboration

• Contribute to internal and open-source GPU libraries

• Present technical contributions at industry conferences (e.g., NVIDIA GTC, AWS re:Invent)

• Strong understanding of GPU architecture and programming paradigms:

  • Memory hierarchy (global, shared, registers, L1/L2 cache)

  • Thread/block/grid organization

  • Synchronization techniques and race condition mitigation

• Proficient in C++ and GPU performance profiling tools

• Knowledge of:

  • CUDA C++ API

  • Memory access patterns and bandwidth optimization

  • Numerical precision and quantization strategies

  • Modern GPU features (e.g., tensor cores, async operations)

• Experience with Transformer models and attention optimization (e.g., Flash Attention)

• Familiarity with GPU kernel libraries: 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