Staff Machine Learning Engineer - ML Training Infrastructure
Job Description
The Role:
We are seeking an experienced, technically strong, impact-driven expert in ML Training Infrastructure with a demonstrated ability to lead through hands-on technical work. In this role, you will be responsible for defining the technical direction and driving the design and development of scalable, reliable, and high-performance AI/ML platform infrastructure that enables advanced AI research and model development at scale.
As a Staff ML Engineer, you will operate as a technical leader across initiatives, partnering closely with machine learning engineers, research scientists, and platform teams to shape architecture, drive major technical decisions, and deliver state-of-the-art AI infrastructure that enables the future of intelligent driving technologies across General Motors vehicles.
What You'll Do:
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Define and drive the architecture, design, and development of scalable, reliable, and high-performance ML frameworks and platform capabilities to support model training at scale.
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Lead model training performance analysis and optimization efforts across distributed training workflows, improving scalability, efficiency, and cost across heterogeneous hardware environments.
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Raise the bar on system observability, debuggability, operational excellence, and developer experience across the ML training stack.
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Own large, ambiguous, cross-functional technical initiatives from strategy through execution, including technical roadmap definition, tradeoff analysis, and delivery.
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Influence platform direction by identifying long-term infrastructure investments, setting engineering standards, and driving adoption of best practices across teams.
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Collaborate across organizational boundaries to align requirements, resolve technical disagreements, and integrate new capabilities into the platform ecosystem.
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Mentor engineers through design reviews, technical guidance, and hands-on partnership, while elevating engineering quality across the team.
Your Skills & Abilities (Required Qualifications)
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Bachelor's degree or higher in Computer Science or a related field, or equivalent practical experience.
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7+ years of professional software engineering experience.
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5+ years of specialized experience in AI/ML infrastructure, such as enabling distributed training for large-scale ML models.
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Strong programming skills in Python, with deep proficiency in frameworks such as PyTorch (preferred), TensorFlow, or similar ML systems.
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Proven experience designing and operating distributed systems for ML training, including distributed computing, GPU computing, and cloud environments (AWS, GCP, Azure).
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Demonstrated track record of leading technically ambiguous, cross-team infrastructure initiatives and driving them to measurable impact.
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Strong architectural judgment and ability to make sound technical tradeoffs across performance, reliability, usability, and cost.
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Willingness to travel to Sunnyvale, CA as needed.
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Comfortable operating in highly ambiguous and dynamic environments.
What Will Give You a Competitive Edge (preferred qualifications):
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7+ years of professional software engineering experience.
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Deep expertise in PyTorch 2.x+ and distributed training frameworks.
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Experience designing and developing training platforms that support FSDP, pipeline parallelism, and other scalable solutions for training large foundational models.
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Experience profiling, analyzing, debugging, and optimizing training and data loading performance at scale.
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Strong record of technical leadership through architecture reviews, roadmap influence, and cross-team execution.
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Excellent communication skills, with the ability to build consensus, navigate controversial decisions, communicate risks clearly, and provide constructive technical feedback.
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Self-motivated, execution-oriented, and motivated by delivering broad organizational im