Senior ML Infra Engineer - Autonomy Platform

Socket.dev; Mountain View (CA); On-site; USD 194,000 - 291,000; Full time

Job summary

Nuro is seeking a skilled engineer to join the ML Infrastructure team in Mountain View, CA. You will help train models at the core of the Nuro Driver™ by building scalable training systems, data pipelines, and cost-aware orchestration.

You will own reliability for critical training and release pipelines, design multi-cluster scheduling, and work closely with autonomy teams to push the boundaries of scalable autonomous driving technology.

Qualifications

• BS/MS/PhD in CS/EE with 3+ years of relevant work experience.
• Strong Python, and comfort with C++, Go or similar systems language.
• Experience running production infrastructure on Kubernetes.
• Solid distributed-systems fundamentals and reliability thinking.

Responsibilities

• Contribute to training infrastructure across multi-generation accelerators and multi-cluster scheduling and orchestration.
• Design and operate large-scale data pipelines - batch and streaming ingestion, storage layout, and high-throughput data generation and storage.
• Design and develop agentic-first ML workflows - data-to-training-to-evaluation pipelines that are introspectable, reproducible, and easy for autonomy teams to run and extend.
• Own reliability for critical training and release pipelines: instrument them, define meaningful alerting, and build the on-call and incident-response practices that let the team catch regressions.

Skills

Python

Kubernetes

Distributed systems

Go

C++

Data pipelines

Monitoring/Observability

On-call/Incident response

Education

BS/MS/PhD in CS/EE

Tools

Kubernetes

Job description

Nuro is seeking a skilled engineer to join the ML Infrastructure team in Mountain View, CA. You will help train models at the core of the Nuro Driver™ by building scalable training systems, data pipelines, and cost-aware orchestration.

You will own reliability for critical training and release pipelines, design multi-cluster scheduling, and work closely with autonomy teams to push the boundaries of scalable autonomous driving technology.