Hybrid opportunity at
ZooxEngineering Manager, ML Performance Optimization
Zoox is designing custom autonomous robotaxis and creating a comprehensive ecosystem for urban mobility services. The organization is currently scaling its fleet deployment and seeks an…
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About This Role
Zoox is designing custom autonomous robotaxis and creating a comprehensive ecosystem for urban mobility services. The organization is currently scaling its fleet deployment and seeks an Engineering Manager, ML Performance Optimization to guide a centralized team of machine learning performance engineers. In this position, the selected individual will shape the strategic roadmap for machine learning inference and training optimization, supporting autonomous vehicle operations and cloud infrastructure. The role involves overseeing…
Job Description
Zoox is designing custom autonomous robotaxis and creating a comprehensive ecosystem for urban mobility services. The organization is currently scaling its fleet deployment and seeks an Engineering Manager, ML Performance Optimization to guide a centralized team of machine learning performance engineers.
In this position, the selected individual will shape the strategic roadmap for machine learning inference and training optimization, supporting autonomous vehicle operations and cloud infrastructure. The role involves overseeing the design and operation of platforms that validate, train, serve, and monitor machine learning models while meeting strict computational and latency budgets. The manager will collaborate with multiple groups including simulation, perception, planning, prediction, and hardware engineering.
This opportunity is well-suited for an experienced engineering leader with a strong background in machine learning acceleration, distributed training, and cross-functional collaboration. The role requires guiding high-performing technical professionals, recruiting new talent, and ensuring reliable, scalable model deployment across vehicles and cloud environments.
Responsibilities
- Formulate and drive a strategic roadmap for ML inference and training performance optimization
- Lead the architecture, implementation, and operation of a scalable ML platform
- Manage end-to-end performance optimization for large-scale training and low-latency inference
- Recruit and mentor engineering talent while cultivating a collaborative team culture
- Partner with research, software, data, and hardware engineering groups to establish requirements and architectural alignment
Requirements
- Minimum of 8 years of professional experience
- At least 3 years of engineering management experience
- Technical expertise in ML performance optimization such as distributed training strategies, mixed-precision training, and model compression
- Background in building user-friendly ML infrastructure for large-scale training and low-latency serving
- Practical experience with training frameworks like PyTorch or JAX utilizing GPUs
Qualifications
- Experience with GPU-accelerated inference platforms such as TensorRT and Ray Serve
- Proven history of cross-functional partnership across research, product, hardware, and platform divisions
Core Skills
Frequently Asked Questions
What is the employment type for this position?
The posting indicates that this is a full-time position.
What is the remote work policy?
The role has a hybrid remote status.
What salary does this position offer?
The compensation details and salary figures are not specified in the job posting.
What kind of experience is required to apply?
Applicants must have at least 8 years of relevant experience, including 3 or more years of management experience overseeing engineers, along with a strong technical background in machine learning performance optimization.
Sample Interview Questions
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