Software Engineer, ML Infrastructure Platform

Nuro; Mountain View (WY); On-site; USD 160,000 - 241,000; Full time

Benefits offered by this job

Annual bonus

Equity

Competitive benefits package

Job summary

Nuro seeks an ML Infrastructure Engineer to join the team building and operating the systems that train our autonomous driving models. You will work on distributed training pipelines, large-scale data ingestion, and reproducible ML workflows that enable rapid iteration across multi-cluster environments.

You will own reliability for critical training and release pipelines, implement monitoring and on-call practices, and collaborate with autonomy teams to improve scalability and cost efficiency.

Qualifications

• BS/MS/PhD in Computer Science, Electrical Engineering, or related field.
• Strong Python skills and experience with systems programming languages.
• Experience with production infrastructure on Kubernetes.
• Understanding of distributed systems and reliability.
• GPU distributed training concepts and NCCL knowledge beneficial.

Responsibilities

• Contribute to ML training infrastructure across multi-cluster environments.
• Design and operate large-scale data pipelines for training workflows.
• Develop agentive-first ML workflows for end-to-end pipelines.
• Own reliability for training and release pipelines with monitoring and runbooks.
• Collaborate with autonomy teams to improve scalability and cost efficiency.

Skills

Python

Kubernetes

Distributed systems

C++/Go

GPU training concepts

GCP

Education

BS/MS/PhD in Computer Science or Electrical Engineering

Tools

NVIDIA NCCL

GPU training tooling

Job description

Who We Are

Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that's why we're building a universal autonomy platform: self-driving for all roads and all rides.

Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles.

With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected.

Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors

About the Role

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 and closed-loop reinforcement learning, to the workflows, orchestration, observability, and cost management that keep the fleet running efficiently.

Our work sits directly on the critical path of autonomy development. When a training run stalls, when a pipeline silently regresses, or when GPU utilization slips, it shows up in how fast the rest of the company can ship. We care as much about reliability and operational maturity as we do about raw scale.

About the Work

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.

About You

BS, MS, or PhD in Computer Science, Electrical Engineering, or a closely related field, plus 1+ years of relevant work experience.

Willingness to deep-dive into implementation and to raise the technical and operational standards of the broader engineering organization.

A demonstrated ownership mindset: you drive systems to operational maturity e.g. through monitoring, alerting, runbooks.

Strong proficiency in Python (and comfort with C++, Go or a similar systems language).

Hands-on experience running production infrastructure on Kubernetes.

Solid distributed-systems fundamentals and the ability to reason about performance, failure modes, and reliability across a complex system.

Bonus Points

Strong working knowledge of GCP.

Experience with building large scale data generation pipelines.

Experience with Kubernetes-native orchestration for ML workloads.

Depth in GPU / distributed training internals, including NCCL and collective communication.

Familiarity with GPU and training observability tooling and using it to diagnose real bottlenecks.

A track record of driving down infrastructure cost while improving reliability.

At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected base pay range is between $160,360 and $240,540 for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package.

At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics.