# Senior Engineering Manager, ML Platform

**Company:** [Zoox](https://hotfix.jobs/companies/zoox)
**Location:** Foster City, CA
**Role:** Engineering Management
**Salary:** $317k – $370k/yr
**Experience:** 10+ years
**Skills:** PyTorch, JAX, TensorRT, Ray Serve, GPU, Distributed Training, ML Infrastructure, Deep Learning Frameworks, Inference Optimization, Reinforcement Learning
**Posted:** 2026-03-09

> Leads engineering team building scalable ML training platform for foundation models and RL, enabling model deployment on robotaxis. Requires 10+ years experience including 4+ in management, expertise in PyTorch/JAX, GPU training, and low-latency inference.

## Job Description

## The Opportunity

The centralized ML Platform team enables innovations across Autonomy and Data Science teams to develop and deploy models on robotaxi and cloud infrastructure, focusing on training and inference optimization.

## In this role, you will:
- **Vision**: Develop and execute a strategic vision for our ML training platform, ensuring scalability, reliability, and performance to support large-scale Foundation and RL models.
- **Technical acumen**: Lead the design, implementation, and operation of a robust and efficient ML training platform to enable the training, experimentation, validation, and monitoring of ML models.
- **Hiring**: Attract, hire, and inspire a diverse world-class engineering team, fostering a culture of innovation, collaboration, and excellence.
- **Partnership**: Collaborate closely with cross-functional teams, including ML researchers, software engineers, data engineers, and hardware engineers to define requirements and align on architectural decisions.
- **Mentorship**: Enable the engineers in the team to grow their careers by providing the right opportunities along with clear and timely feedback.

## Qualifications
- 10+ years of relevant experience, including 4+ years of management experience managing other managers and engineers.
- Experience building user-friendly ML Infrastructure that enabled large-scale model training and high-throughput, low-latency serving use cases.
- Experience with training frameworks like PyTorch, JAX, etc., leveraging GPUs for distributed model training.
- Experience with GPU-accelerated inference using TensorRT, Ray Serve, or similar frameworks.

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