Senior Software Engineer (Machine Learning Performance)

TL;DR

Senior Software Engineer (ML Performance): Delivering a high-performance compute platform for autonomous driving software with an accent on ML model performance optimization and maintainable, high-quality solutions. Focus on designing technical roadmaps, optimizing compute performance at the compiler and infrastructure levels, and leading small engineering teams.

Location: Hybrid work schedule in Hsinchu City or Taipei City, Taiwan

Salary: $3,800,000—$4,370,000 TWD

Company

Waymo is an autonomous driving technology company building the World's Most Experienced Driver to improve mobility and safety.

What you will do

• Act as Technical Lead for a small team of ~5 engineers focusing on individual ML model performance optimization.
• Set the technical vision, direction, and multi-quarter roadmap for ML performance optimization.
• Drive and coordinate large, open-ended technical projects spanning multiple application and infrastructure teams.
• Analyze ML model performance and prototype solutions at the application, compiler, or infrastructure level (firmware, runtime, framework).
• Influence infrastructure teams to prioritize and land performance-critical optimizations using solid methodologies.
• Provide technical mentorship and guidance to team members to ensure technical excellence and code health.

Requirements

• MS degree in Computer Science, Electrical Engineering, or equivalent practical experience.
• 5+ years of experience writing complex C++ and Python code.
• 7+ years of experience in optimizing compute performance for ML applications.
• Experience in compute architectures, performance analysis, and optimization methodologies.
• Must be based in or able to work from Hsinchu City or Taipei City, Taiwan.
• Ability to communicate effectively with cross-functional teams and manage timezone differences.

Nice to have

• Experience in ML modeling and ML compiler implementation.
• Experience with performance tools, simulators, and HW/SW codesign.
• Proficiency in building collaborations across application and infrastructure teams.
• Experience with pruning, quantization, and other model performance optimization techniques.

Culture & Benefits

• Hybrid work schedule.
• Discretionary annual bonus program.
• Equity incentive plan.
• Generous company benefits program.