Job Description

The Role

We are looking for an ML tooling engineer to build tools to analyze and optimize distillation, training, and inference of ML models. You will develop and enhance GM's internal ML tooling for high performance software by leveraging state of the art tools like Nsight Systems, PyTorch, etc.

The Autonomous Vehicle (AV) software stack heavily relies on machine learning models to perform critical driving tasks. These cutting-edge custom ML models require an ecosystem of in-house tooling to analyze and improve them. In this role, you will collaborate closely with engineers and researchers from different AV Engineering teams (e.g., Computer Vision, Perception, Behavioral Prediction) to scope out system requirements, while engaging with AV hardware teams to understand the target hardware platform and its constraints.

What You’ll Be Doing (Responsibilities)

Identify new opportunities to improve both training and inference efficiency
Build workflows for correctness and performance analysis on physical in-car compute and sensors
Build tooling to predict model performance based on architecture and data shape
Build tooling to trace actual performance on large distributed training and distillation jobs, running on the world’s most powerful GPUs, and analyze the results
Continually evolve the toolchain and stack, to leverage the latest advancements in AI
Influence model architecture decisions and strategy within GM

Your Skills & Abilities (Required Qualifications)

3+ years of experience in the field of AI/ML
Experience with ML frameworks (e.g., PyTorch, TensorFlow) and NVIDIA developer ecosystem (TensorRT, Nsight-systems, Nsight-compute))
Expertise in writing production quality Python/C++ code
Expertise in the software development life-cycle - coding, debugging, optimization, testing, integration
BS, or higher degree, in CS/CE/EE, or equivalent

What Will Give You A Competitive Edge (Preferred Qualifications)

Experience developing and deploying machine learning models
GPU programming (CUDA) and familiarity with ML SW stack (e.g., cuDNN, cuBLAS)
Experience with ML accelerators and hardware architecture
3+ years of experience in the field of AI/ML or relevant experience

Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of New York, Colorado, California, or Washington.

· The salary range for this role: is $144,700 to $261,300. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.

· Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.

· Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.

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This role is based remotely, but if the selected candidate lives within a specific mile radius of a GM hub, they will be expected to report to the location three times a week {or other frequency dictated by your manager}.

The selected candidate will be required to travel <25% for this role.

This job may be eligible for relocation benefits.



About GM

Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.

Why Join Us

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Benefits Overview

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We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire.

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