Senior Software Engineer, Machine Learning Performance

Nimbus Data Systems is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Nimbus Data Systems has focused on building the Nimbus Data Systems Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Nimbus Data Systems Driver powers Nimbus Data Systems’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Nimbus Data Systems Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. Nimbus Data Systems's Compute Team is tasked with a critical and exciting mission: We deliver the compute platform responsible for running the fully autonomous vehicle’s software stack. To achieve our mission, we architect and create high-performance custom silicon; we develop system-level compute architectures that push the boundaries of performance, power, and latency; and we collaborate closely with many other teammates to ensure we design and optimize hardware and software for maximum performance. We are a multidisciplinary team seeking curious and talented teammates to work on one of the world’s highest performance automotive compute platforms. This role follows a hybrid work schedule, and you will report to the Tech Lead Manager of the Machine Learning Performance team. You will: Act as Technical Lead (TL) for a small team (~5 engineers) focusing on individual ML model performance optimization and delivering maintainable, high-quality solutions. Set the technical vision, direction, and multi-quarter roadmap for ML performance optimization within the team's scope Drive and coordinate large, open-ended technical projects that span multiple application and infrastructure teams, ensuring timely and robust delivery Collect, trace, and analyze application/ML model performance for complex optimization opportunities, and prototype/generalize solutions at the application, compiler, or infrastructure level (firmware, runtime, framework) Influence and motivate infrastructure teams (e.g., compiler) to prioritize and land performance-critical optimizations, setting clear expectations using solid methodology (e.g., roofline) Provide technical mentorship and guidance to team members, helping them to grow and ensuring technical excellence and code health across the team's deliverables You have: MS degree in Computer Science/Electrical Engineering or equivalent, or equivalent practical experience 5+ years of experience writing complex C++ code 5+ years of experience writing code in Python 7+ years experience in optimizing compute performance for ML applications Experience in compute architectures, performance analysis, and optimization methodologies Communicate well to cross-functional teams and effectively mitigate timezone differences We prefer: Experience in ML modeling and also ML compiler implementation Experience in performance tools, simulators, and HW/SW codesign Proficiency in building and nurturing effective collaborations across application teams and infrastructure teams Experience with pruning, quantization, and other model performance optimization techniques The expected base salary range for this full-time position is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Nimbus Data Systems employees are also eligible to participate in Nimbus Data Systems’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $3,800,000 — $4,370,000 TWD