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1–3 jobs· newest first

Research Scientist Intern, TPU(PhD)
Meta · Menlo Park, US
On-site · Internship · Intern
GPU Compiler Intern
NVIDIA · Zurich, Switzerland
Internship · Intern
GPU Compiler Intern
NVIDIA · Zurich, Switzerland
Internship · Intern

+10K new in the last hour

Research Scientist Intern, TPU(PhD)

Meta · Posted Nov 11, 2026

Menlo Park, California, United States
On-site
Internship · Intern
0+ years

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The PyTorch TPU team focuses on enabling efficient and accurate inference of 1P models directly on TPU.

Responsibilities

Develop, evaluate methodologies and systems for running LLM/Multi-modality models cost-effectively on TPU systems.
Analyze and extract insights from traffic pattern to derive computational intrinsics that can be leveraged into actions in system and kernel optimization.

Requirements

PhD in Computer Science
PhD in Computer Engineering
PhD in Electrical Engineering
PhD in related field

Skills

Python system and kernel optimization GPU/TPU/ASICs Triton Cuda Cutedsl Pallas performance modeling

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GPU Compiler Intern

NVIDIA · Posted Nov 11, 2026

Zurich, Zurich, Switzerland
Internship · Intern

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Develop compiler infrastructure for Python-based GPU programming, including DSL design, MLIR IR design, and compiler optimizations.

Responsibilities

Design and implement Python DSL features for GPU kernel authoring
Develop MLIR dialects, transformation passes, and lowering pipelines
Design and optimize GPU kernels
Profile, benchmark, and validate compiler and kernel improvements

Requirements

Bachelor’s in Computer Science
Master’s in Computer Science
Ph.D. in Computer Science
Bachelor’s in Computer Engineering
Master’s in Computer Engineering

Skills

Python C++MLIR CUDA GPU programming profiling performance analysis optimization CUTLASS

GPU Compiler Intern

NVIDIA · Posted Nov 11, 2026

Zurich, Zurich, Switzerland
Internship · Intern

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Responsibilities

Design and implement Python DSL features for GPU kernel authoring, including metaprogramming and IR generation.
Develop MLIR dialects, transformation passes, and lowering pipelines that preserve the information needed for effective GPU optimization.
Design and optimize GPU kernels, exploring how IR design and compiler transformations affect generated code and performance.
Profile, benchmark, and validate compiler and kernel improvements across representative workloads.

Requirements

Currently pursuing or holding a Bachelor’s, Master’s, or Ph.D. degree in Computer Science, Computer Engineering, or a related technical discipline, or equivalent practical experience.
Nice to have: CUTLASS
Nice to have: CuTeDSL
Nice to have: Triton
Nice to have: LLVM

Skills

Python C++MLIR CUDA GPU programming profiling

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performance analysis

memory access patterns

parallel execution

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