Our ML Compiler team builds the software that runs PyTorch models on Samsung’s compute-near-memory AI accelerator: a PyTorch device and compiler backend, a Triton-based kernel compiler and an LLVM back end. We work closely with the runtime and hardware teams and with the PyTorch and Triton open-source communities. Over the summer you’ll own one piece of making ordinary PyTorch models fast on new memory-centric hardware, from research idea to merged, measured code.

Location: Daily onsite presence at our San Jose Headquarters in alignment with our Flexible Work policy

Reports to: Director

Design a search space for Triton kernel configurations on the accelerator: tile sizes, launch-grid mapping, on-chip buffer budgets and load style.

Build legality rules with our compiler engineers that prune configurations that will not compile or will not fit in on-chip memory.

Develop a kernel cost model, calibrated against our simulator and profiler, that ranks candidates so only the most promising are benchmarked.

Build a reliable benchmarking harness and a persistent tuning cache, so that tuning cost stays practical.

Integrate the tuner with the torch.compile Inductor autotuning path and with Triton’s autotuner.

Extend tuning to data placement: tune kernels for a given tensor layout and export tuned costs that a placement pass can use.

Measure kernel-level and end-to-end LLM inference gains, and share results in weekly syncs and a final presentation.

Collaborate with PyTorch and Triton developers on upstream autotuning hooks, and hand off documented, tested code.

Complete other responsibilities as assigned.

Intern, ML Compiler (Kernel Auto-Tuning)

Responsibilities

Benefits

Company

Samsung Semiconductor

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