ML Infrastructure Engineer - Autonomous Driving

Socket.dev; Mountain View (CA); On-site; USD 160,000 - 241,000; Full time

Job summary

Nuro takes a machine-learning-first approach to autonomous driving, and the ML Infrastructure team builds and operates the infrastructure that makes that possible. We own the systems that train the models at the core of the Nuro Driver—from distributed GPU training to the workflows, orchestration, observability, and cost management that keep the fleet running efficiently.

As part of the team, you will contribute to training infrastructure across multi-generation accelerators and multi-cluster

Qualifications

• BS, MS, or PhD in Computer Science, Electrical Engineering, or a closely related field.
• Willingness to deep-dive into implementation and raise technical/operational standards.
• Demonstrated ownership mindset driving systems to operational maturity.
• Strong proficiency in Python (and comfort with C++, Go or similar).
• Hands-on experience running production infrastructure on Kubernetes.
• Solid distributed-systems fundamentals and reliability reasoning across complex systems.

Responsibilities

• Contribute to Nuro's training infrastructure, spanning 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

On-call / incident response

C/C++ or Go familiarity

Performance & reliability

Monitoring & alerting

Education

BS, MS, or PhD in CS/EE or related

Job description

Nuro takes a machine-learning-first approach to autonomous driving, and the ML Infrastructure team builds and operates the infrastructure that makes that possible. We own the systems that train the models at the core of the Nuro Driver—from distributed GPU training to the workflows, orchestration, observability, and cost management that keep the fleet running efficiently.

As part of the team, you will contribute to training infrastructure across multi-generation accelerators and multi-cluster