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# Software Engineer, GPU Code Generation for Machine Learning Inference

- **Company:** NVIDIA Ltd.
- **Location:** Santa Clara, United States
- **Experience:** Expert
- **Salary:** $152,000.0 - $241,500.0
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, C++ (Programming Language), Code Generation, Nvidia CUDA, Computer Engineering, Software Debugging, Python (Programming Language), Software Engineering, Graphics Processing Unit (GPU), Pytorch, Large Language Models, Deep Learning, Parallel Computation, Gpu Programming, Discretization, Information Technology, ONNX (Open Neural Network Exchange) Format, SGLang, TensorRT, VLLM, CUTLASS, MLIR (Multi-Level Intermediate Representation)
- **Published:** October 22, 2026
- **Apply:** https://startup.jobs/senior-software-engineer-gpu-code-generation-for-machine-learning-inference-2100-nvidia-usa-10540715

## About the Role

As a Senior Software Engineer, you will develop advanced GPU inference software for TensorRT. We're looking for engineers with strong experience in GPU optimization and compiler development who can translate a deep understanding of GPU execution into efficient code generation and measurable improvements in generative AI inference., * BS, MS, or PhD in Computer Science, Computer Engineering, or a related field, or equivalent experience.

 * 4+ years of relevant software development experience, including contributions to a substantial codebase or project.
 * Strong proficiency in C++ and experience building performance-critical software.
 * Understanding of GPU architecture and parallel programming concepts, including memory hierarchies, thread execution, synchronization, and resource utilization.
 * A rigorous approach to performance analysis and debugging, with the ability to validate numerical correctness and explain performance tradeoffs.
 * Strong problem-solving and communication skills, and the ability to work independently and collaborate across teams.

Ways to stand out from the crowd:

 * Experience developing or optimizing GPU kernels using CUDA, Triton, CuTe DSL, CUTLASS, or other GPU programming languages.
 * Experience with MLIR compiler infrastructure, familiarity with intermediate representations, lowering, optimization passes, and GPU code generation.
 * Familiarity with NVIDIA Tensor Core architecture, mixed-precision computation, quantization, or low-level GPU performance analysis using tools such as NVIDIA Nsight.
 * Experience developing inference frameworks such as PyTorch, JAX, ONNX Runtime, TensorRT, TensorRT-LLM, vLLM, and SGLang.
 * A track record of measurable performance improvements in production software or contributions to GPU programming and compiler projects.

## Description

* Design and implement efficient GPU code generation techniques in TensorRT that balance generated code performance, compilation overhead, and startup latency.
 * Develop compiler optimizations, including operator fusion, dynamic scheduling, and specialization for model shapes, data types, and GPU architectures.
 * Profile workloads and analyze generated GPU code to identify bottlenecks and guide compiler and runtime improvements.
 * Develop robust, maintainable software in C++, and Python for tooling, testing, and performance analysis.
 * Collaborate with GPU architects, kernel developers, compiler engineers, and deep learning experts to guide hardware and software design.