This page was automatically translated and may contain errors. View in English.
高通

AI Performance Engineer (Competitive & Network Analysis)

Qualcomm

Riyadh, Riyadh Province, Saudi Arabia · 全职

抢先申请

经验
2年以上
薪水
职位空缺
1
发布
1 小时前
工作模式
在办公室
学历
Master's degree
恢复
需要申请

你的工作地点

职位描述

About Qualcomm

Qualcomm is expanding its presence in Riyadh as part of Saudi Arabia's Vision 2030 digital transformation, investing in advanced computing and data centre capabilities to power AI, cloud, and connectivity at scale. This role offers a chance to contribute to the development of data centre operations and Cloud AI technology in the region.

Role Overview

We are seeking an AI Performance Engineer to join a collaborative team working on the entire product lifecycle from research to deployment. The position involves optimizing AI model inference, working with advanced generative AI algorithms, and collaborating across multiple teams to enhance AI workload performance on current and future hardware platforms.

Main Responsibilities

  • Optimize and deploy AI models using frameworks like PyTorch and ONNX for efficient inference.
  • Analyze and improve performance of large language, vision-language, and diffusion models considering throughput and latency.
  • Map next-generation AI workloads to hardware architectures to maximize efficiency.
  • Collaborate closely with customers and internal teams including compiler, firmware, and platform experts.
  • Diagnose and resolve complex issues affecting performance or stability.
  • Develop engineering solutions for continuous AI workload performance insights and improvements.
  • Design and implement efficient, high-level kernels using tools like Triton.

Required Qualifications and Skills

  • Proven experience in building and optimizing language models, preferably in production using PyTorch and ONNX.
  • Strong understanding of transformer models, attention mechanisms, and their performance trade-offs.
  • Experience with workload distribution techniques such as sharding and parallelism.
  • Advanced Python programming capabilities.
  • Active engagement with latest inference optimization methods.
  • Knowledge of computer architecture, machine learning accelerators, in-memory processing, and distributed systems.
  • Excellent communication and problem-solving skills; ability to thrive in a fast-paced, collaborative environment.
  • Master's degree in Computer Science, Machine Learning, Computer Engineering or Electrical Engineering.

Preferred (Bonus) Qualifications

  • Background in neural network operators, linear algebra, and mathematical libraries.
  • Familiarity with machine learning compilers.
  • Experience in balancing model accuracy and evaluation methodologies.
  • Knowledge of torch.compile or torchDynamo utilities.
  • PhD in Computer Science, Computer Engineering, or Machine Learning.

Compensation and Benefits

  • Competitive salary including housing and transportation allowances.
  • Stock options (RSUs) and performance bonuses.
  • 16 weeks fully paid maternity leave and 6 weeks fully paid paternity leave.
  • Employee stock purchase program.
  • Child education allowance.
  • Relocation and immigration assistance if necessary.
  • Life and medical insurance coverage.
  • Wellness reimbursement for health and fitness memberships.

Minimum Eligibility

  • Bachelor's degree plus 4+ years relevant systems engineering experience, or
  • Master's degree plus 3+ years relevant systems engineering experience, or
  • PhD plus 2+ years relevant systems engineering experience.
  • Equivalent experience considered if candidate demonstrates required competencies.

Additional Information

Qualcomm is committed to equal opportunity employment and accessibility, offering accommodations for candidates with disabilities. Employees are expected to comply with company policies regarding confidentiality and security. Recruitment agencies are not authorized to submit applications on behalf of candidates.

Work styles they’re looking for

沟通 适应性 问题解决 合作 Proactive learning

如果您希望收到回复,请留下您的信息——我们不会将您的信息用于其他用途。

点击浏览拖放,或 粘贴 截图

PNG、JPG、GIF、MP4、WebM、MOV 格式 · 每个文件最大 20MB · 最多 5 个文件

🤖
在线·即时人工智能帮助