Engineering Manager, ML Training Infrastructure

📋 Job Overview

Woven by Toyota is enabling Toyota’s once-in-a-century transformation into a mobility company. Inspired by a legacy of innovating for the benefit of others, our mission is to challenge the current state of mobility through human-centric innovation — expanding what “mobility” means and how it serves society.

Our work centers on four pillars: AD/ADAS, our autonomous driving and advanced driver assist technologies; Arene, our software development platform for software-defined vehicles; Woven City, a test course for mobility; and Cloud & AI, the digital infrastructure powering our collaborative foundation. Business-critical functions empower these teams to execute, and together, we’re working toward one bold goal: a world with zero accidents and enhanced well-being for all.

🏢 About Woven by Toyota

Enterprise AI is a platform that provides end-to-end machine learning tooling experience to support and accelerate machine learning development, including autonomous driving and other related projects. Our platform serves customers as a standardized machine learning platform within Woven by Toyota as the larger Toyota Group companies.

The Enterprise AI ML Training Infrastructure team builds and operates the infrastructure that enables engineers and researchers to train, evaluate, and iterate on machine learning models at scale. Our customers include teams working on AD/ADAS and Woven City, where large-scale machine learning workloads, simulation, and data processing are critical to developing the next generation of mobility technologies.

The team operates at the intersection of machine learning, distributed systems, cloud infrastructure, and developer platforms. Our systems include large-scale GPU compute environments, Kubernetes-based cluster infrastructure, and workflow and pipeline orchestration technologies such as Ray, Airflow, and Temporal.

🎯 The Role

We are looking for an Engineering Manager to lead a team to help shape the technical direction, engineering culture, and long-term evolution of our ML training infrastructure platform.

✅ Key Responsibilities

• Lead engineers and foster a collaborative and innovative environment.
• Manage and nurture a group of around 7–8 engineers, offering technical vision, mentorship, professional growth, and effective project delivery.
• Establish and articulate the technical vision and future roadmap for machine learning training infrastructure.
• Direct the architecture, construction, and operation of resilient systems supporting massive ML model training workloads.
• Construct and maintain Kubernetes-driven compute clusters and container ecosystems tailored for machine learning workloads.
• Oversee and optimize GPU fleets, addressing cluster scheduling, resource utilization, system stability, and cost performance.
• Advance distributed model training capabilities and workload management leveraging frameworks like Ray and adjacent tools.
• Enhance workflow orchestration platforms by deploying and supporting solutions such as Airflow, Temporal, and similar pipeline frameworks.
• Elevate the system resilience, monitoring insights, elasticity, and operational productivity of our ML training stack.
• Work closely alongside AD/ADAS, Woven City, and internal platform partners to gather feature requests and ship infrastructure solutions that accelerate development.
• Partner with cross-functional engineering groups to specify platform interfaces, APIs, system architectures, and operational standards.
• Translate high-level organizational objectives and user requirements into concrete, executable engineering milestones with product leads.
• Institute rigorous software practices across implementation, code reviews, automated testing, continuous deployment, and live site management.
• Engage in production support workflows, lead post-incident reviews, and champion system refinements based on operational findings.

🎁 Benefits

Competitive

🛂 Visa & Eligibility

Not specified