热门公司

NVIDIA
NVIDIA

Pioneering accelerated computing and AI

Senior Deep Learning Software Engineer, Inference and Model Optimization

职能机器学习
级别资深
地点Us, Canada, United States
方式现场办公
类型全职
发布2个月前
立即申请

必备技能

Python

PyTorch

Machine Learning

NVIDIA is at the forefront of the generative AI revolution! The Algorithmic Model Optimization Team specifically focuses on optimizing generative AI models such as large language models (LLM) and diffusion models for maximal inference efficiency using techniques ranging from neural architecture search and pruning to sparsity, quantization, and automated deployment strategies. Our work includes conducting applied research to improve model efficiency as well as developing an innovative software platform (TRT Model Optimizer). Our software is used both internally across NVIDIA and externally by research and engineering teams alike developing best-in-class AI models.

We are now looking for a Senior Deep Learning Software Engineer to develop and scale up our automated inference and deployment solution. As part of the team, you will be instrumental in pushing the limits of inference efficiency and large-scale, automated deployment. Your work will touch upon fundamental aspects of a typical machine learning stack including working in high-level frameworks like Py Torch and Hugging Face to developing and improving high-performance kernel implementations in CUDA, TRT-LLM, and Triton. This is an exceptional opportunity for passionate software engineers straddling the boundaries of research and engineering, with a strong background in both machine learning fundamentals and software architecture & engineering.

What you’ll be doing:

  • Train, develop, and deploy state-of-the generative AI models like LLMs and diffusion models using NVIDIA's AI software stack.

  • Leverage and build upon the torch 2.0 ecosystem (Torch Dynamo, torch.export, torch.compile, etc...) to analyze and extract standardized model graph representation from arbitrary torch models for our automated deployment solution.

  • Develop high-performance optimization techniques for inference, such as automated model sharding techniques (e.g. tensor parallelism, sequence parallelism), efficient attention kernels with kv-caching, and more.

  • Collaborate with teams across NVIDIA to use performant kernel implementations within our automated deployment solution.

  • Analyze and profile GPU kernel-level performance to identify hardware and software optimization opportunities.

  • Continuously innovate on the inference performance to ensure NVIDIA's inference software solutions (TRT, TRT-LLM, TRT Model Optimizer) can maintain and increase its leadership in the market.

  • Play a pivotal role in architecting and designing a modular and scalable software platform to provide an excellent user experience with broad model support and optimization techniques to increase adoption.

What we need to see:

  • Masters, PhD, or equivalent experience in Computer Science, AI, Applied Math, or related field.

  • 3+ 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, Py Torch, and related ML tools (e.g. Hugging Face).

  • 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 from the crowd:

  • Contributions to Py Torch, 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 in writing high-performance GPU kernels for machine learning workloads in frameworks such as CUDA, CUTLASS, or Triton.

Increasingly known as “the AI computing company” and widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. Are you creative, motivated, and love a challenge? If so, we want to hear from you! Come, join our model optimization group, where you can help build real-time, cost-effective computing platforms driving our success in this exciting and rapidly-growing field.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until March 1, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

浏览量

0

申请点击

0

Mock Apply

0

收藏

0

关于NVIDIA

NVIDIA

NVIDIA

Public

A computing platform company operating at the intersection of graphics, HPC, and AI.

10,001+

员工数

Santa Clara

总部位置

$4.57T

企业估值

评价

10条评价

4.4

10条评价

工作生活平衡

2.8

薪酬

4.5

企业文化

4.2

职业发展

4.3

管理层

3.8

78%

推荐率

优点

Cutting-edge technology and innovation

Excellent compensation and benefits

Great team culture and collaboration

缺点

High pressure and expectations

Poor work-life balance and long hours

Fast-paced environment leading to burnout

薪资范围

79个数据点

L3

L4

L5

L3 · Data Scientist IC2

0份报告

$177,542

年薪总额

基本工资

-

股票

-

奖金

-

$150,910

$204,174

面试评价

5条评价

难度

3.0

/ 5

面试流程

1

Application Review

2

Recruiter Screen

3

Technical Phone Screen

4

Onsite/Virtual Interviews

5

Team Matching

6

Offer

常见问题

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Past Experience