Senior ML Compiler Engineer, Compute at Waymo UK Ltd

Bangalore, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

04 Jul, 26

Salary

7250000.0

Posted On

05 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Compiler optimization, C++, Python, Machine learning inference, Linear algebra, Neural network architecture, Hardware/software codesign, Parallel architectures, Performance analysis, System-level compute architecture, Custom silicon, Inference efficiency, Neural network algorithms

Industry

technology;Information and Internet

Description

Waymo 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, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo 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. Waymo's Compute Team is tasked with a critical and exciting mission: We deliver the compute platform responsible for running the 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 with many other teammates to ensure we design and improve hardware and software for maximum performance. We are a diverse team looking for 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 a Software Engineering Manager. This position will require the ability to work some hours that align with the team in the Pacific Time zone on an as needed basis. You will: Analyze the performance characteristics of code generated by our production grade compiler, and design and implement optimizations to improve that performance Design and implement compiler support for novel features of our high-performance architecture Work with hardware architects to understand and influence the development of our unique neural network inference platform through hardware/software codesign Work with model developers to tune their neural networks for better inference efficiency and accuracy You have: BS degree in Computer Science/Electrical Engineering or equivalent experience and 5+ years of industry experience OR MS degree in Computer Science/Electrical Engineering and 3+ years of industry experience PhD degree in Computer Science/Electrical Engineering and 1+ years of industry experience 3+ years experience working on compilers for parallel architectures 1+ years experience working with ML inference or linear algebra computation C++ programming skills We prefer: Python programming experience Experience with compilers for neural networks Knowledge of computer architectures used for neural network inference, and neural network performance characteristics Knowledge of the principles behind popular machine learning and neural network algorithms and applications 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. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range ₹6,100,000—₹7,250,000 INR

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Responsibilities

You will analyze and optimize code generated by production-grade compilers and design compiler support for high-performance architectures. Additionally, you will collaborate with hardware architects and model developers to improve neural network inference efficiency through hardware/software codesign.