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资深机器学习性能工程师(训练效率)

机器翻译
查看雇主原标题Staff ML Performance Engineer (Training Efficiency)

Wayve · Sunnyvale · $336k – $359k

职位信息来自雇主公开的招聘页面。申请前请务必在雇主官网核实详情。

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发现指数 67/100,仅依据与该职位一起存储的证据计算。

67/100 发现指数
  • 新的雇主官方职位
  • 已披露薪资
  • 稀有职位匹配

分数构成

  • 时效性 (随职位发布时间变化)+18
  • 雇主官方来源+15
  • 已披露薪资+15
  • 稀有职位+11
  • 公司来源健康度+8

该职位未包含:远程职位、提及签证担保、提及搬迁、未出现在监控的职位板上。

这些理由来自雇主自己的职位描述与我们核实过的来源检查结果。除了已存储的信号之外,我们不做任何推测。

职位描述

英文原文

该职位由雇主以英文发布,暂无中文版本,下面完整显示英文原文。 查看官方职位页面.

职位描述

About us

Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.

Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.

In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.

At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.

Make Wayve the experience that defines your career!

The role

We are looking for a Staff ML Performance Engineer to join our Training Tech team working on optimizing large scale ML jobs to enable scaling our models to the next order of magnitude. A successful candidate will increase efficiency of training and inference workloads in order to allow Wayve to train larger models faster.

Key responsibilities:

• Profile ML workloads to identify their bottlenecks, e.g. using NVIDIA Nsight Systems

• Design and implement efficiency improvements to maximize MFU and throughput, e.g. parallelism, model compilation, mixed precision

• Design and implement observability tools to identify bottlenecks and drive performance improvements, e.g. to track MFU, throughput, latency, etc

• Design and implement benchmarking tools, e.g. to track efficiency gains or regressions

• Collaborate closely with Research teams to integrate training efficiency improvements and create a culture of perfo

岗位职责

We are looking for a Staff ML Performance Engineer to join our Training Tech team working on optimizing large scale ML jobs to enable scaling our models to the next order of magnitude. A successful candidate will increase efficiency of training and inference workloads in order to allow Wayve to train larger models faster.

• Profile ML workloads to identify their bottlenecks, e.g. using NVIDIA Nsight Systems

• Design and implement efficiency improvements to maximize MFU and throughput, e.g. parallelism, model compilation, mixed precision

• Design and implement observability tools to identify bottlenecks and drive performance improvements, e.g. to track MFU, throughput, latency, etc

• Design and implement benchmarking tools, e.g. to track efficiency gains or regressions

• Collaborate closely with Research teams to integrate training efficiency improvements and create a culture of performance optimization

任职要求

In order to set you up for success in this role, we’re looking for the following skills and experience.

• 10+ years of industry experience driving performance engineering across ML systems, GPU compute infrastructure, distributed platforms or similar field.

• Experience optimizing large scale jobs on GPU compute clusters.

• Experience in working in platform teams and working with research teams.

• Experience in writing, reporting, and tracking performance benchmarks in an open and accessible way.

• Ability to write high quality, well-structured and tested Python code

• BS or MS in Machine Learning, Computer Science, Engineering, or a related technical discipline or equivalent experience

Desirable

• Experience working with concurrent, parallel and distributed computing.

• Experience using NVIDIA NSight Systems or other system profilers.

• Experience implementing GPU kernels (CUDA, Triton, etc).

• Knowledge of computing fundamentals - what makes code fast, secure and reliable.

This role is a full-time role based in Sunnyvale, CA (hybrid) and the reasonably estimated salary for this role ranges from $336,400 to $359,000, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience.

#LI-HH1 Wayve is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know.

We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply.

At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law.

For more information visit Careers at Wayve.

To learn more about what drives us, visit Values at Wayve

For US candidates only, please visit E-Verify Notice and Participation and Right to Work

DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.

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