Senior Deep Learning Software Engineer

Details

We are looking for a Senior Deep Learning Software Engineer to design and build our automated inference and deployment solution. You will help define a scalable architecture for deep learning inference with emphasis on ease-of-use and compute efficiency. Work spans multiple layers of the DL deployment stack: developing features in high-level frameworks (PyTorch, JAX), designing and implementing a high-performance execution environment, low-level GPU optimizations, and developing custom GPU kernels in CUDA and/or Triton.

Responsibilities

• Define and implement a modular, scalable platform to bridge training and deployment workflows and enable tight integration of deployment tooling with training frameworks such as Megatron and NeMo.
• Leverage and extend the Torch 2.0 ecosystem (TorchDynamo, torch.export, torch.compile, etc.) to analyze and extract standardized model graph representations from arbitrary torch models for automated deployment.
• Develop support for inference optimization techniques such as speculative decoding and LoRA.
• Collaborate with teams across NVIDIA to integrate performant kernel implementations into the automated deployment solution.
• Analyze and profile GPU kernel-level performance to identify hardware and software optimization opportunities.
• Continuously innovate on inference performance to ensure NVIDIA's inference software solutions (TensorRT, TRT-LLM, TRT Model Optimizer) maintain and increase market leadership.

Requirements

• Master's, PhD, or equivalent experience in Computer Science, AI, Applied Math, or related field.
• 8+ years of relevant work or research experience in deep learning.
• Excellent software design skills, including debugging, performance analysis, and test design.
• Strong proficiency in Python, PyTorch, and related ML tools.
• Strong algorithms and programming fundamentals.
• Good written and verbal communication skills and the ability to work independently and collaboratively in a fast-paced environment.

Ways to stand out

• Contributions to PyTorch, JAX, or other machine learning frameworks.
• Knowledge of GPU architecture and compilation stack, and capability of understanding and debugging end-to-end performance.
• Familiarity with NVIDIA's deep learning SDKs such as TensorRT.
• Prior experience writing high-performance GPU kernels for ML workloads in CUDA, CUTLASS, or Triton.

Compensation & Benefits

• Base salary range: 224,000 USD - 356,500 USD (final base salary determined by location, experience, and pay of employees in similar positions).
• Eligible for equity and company benefits (link provided in original posting).

Additional information

• Location: Santa Clara, California, United States. #LI-Hybrid
• Applications accepted at least until February 3, 2026.
• NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer.