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About the role
You will write, evaluate, and profile specialized compute kernels that run on a custom AI accelerator. This is the critical interface between high-level ML workloads and silicon — your code directly determines how effectively the hardware performs. You'll work closely with the architecture and compiler teams to define the kernel programming model, implement core tensor operations, and drive the performance profiling workflow that validates silicon design decisions.
What they're looking for
- C/C++ — production-grade systems code, not scripted glue. You'll write performance-critical kernels
- CUDA or equivalent accelerator programming — deep experience writing GPU kernels, understanding warp/wavefront execution, memory coalescing, shared memory optimization. The mental model transfers directly
- Computer architecture — you need to reason about pipelines, memory hierarchies, data movement costs, and how software maps to hardware
- Performance profiling and optimization — you live in profilers. Identifying bottlenecks, measuring throughput, and iterating until kernels meet targets is the core loop
- Tensor operations — practical understanding of GEMM, convolution, attention, reduction, and scatter/gather as they map to hardware
- Python — for scripting, DSL integration, and profiling automation
More about this role
You will write, evaluate, and profile specialized compute kernels that run on a custom AI accelerator. This is the critical interface between high-level ML workloads and silicon — your code directly determines how effectively the hardware performs. You'll work closely with the architecture and compiler teams to define the kernel programming model, implement core tensor operations, and drive the performance profiling workflow that validates silicon design decisions.
- Write and optimize compute kernels for a custom AI accelerator — tensor operations, data movement patterns, memory hierarchy exploitation
- Develop and maintain profiling infrastructure to measure kernel performance against architectural targets
- Define and document shuffle patterns for ML kernel primitives across CPU-like control, tensor cores, and CUTLASS-style operations
- Drive kernel DSL design decisions — thread spawn mechanisms, register passing conventions, and memory management strategies
- Enable end-to-end kernel execution on the architectural simulator
- Collaborate with the compiler team on the MLIR dialect — your kernels are the primary validation target
- Create onboarding documentation and kernel...
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