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Nuro

Software Engineer, ML Inference Platform

Mountain View, California (HQ)From $160.4kmidAdded 2 days ago

About this role

Nuro seeks a Software Engineer to build and maintain core ML infrastructure for autonomous driving, including model training pipelines, inference platforms, and compiler systems that power self-driving robots at scale.

What you'll do

  • Design and develop ML workflow pipelines for training, optimizing, validating, and deploying autonomy models
  • Build continuous testing and monitoring systems for ML infrastructure components
  • Develop observability and tracking systems for model lifecycles from data generation to on-road validation
  • Maintain in-house ML inference platform serving large language models efficiently
  • Maintain in-house ML compiler platform for compiling and deploying models to various hardware
  • Collaborate with autonomy teams across Nuro to design scalable infrastructure solutions

What they're looking for

  • Python proficiency
  • C++ proficiency (strongly preferred)
  • ML pipeline design and development
  • Distributed systems architecture
  • Data workflow orchestration
  • ML compiler experience
  • System performance optimization
  • Observability and monitoring design

Benefits

  • Base salary $160,360–$240,540 annually
  • Annual performance bonus eligible
  • Equity compensation
  • Competitive benefits package
  • Work on cutting-edge autonomous vehicle technology
  • Collaborative environment with leading investors backing the company
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Nuro

Nuro builds autonomous vehicle platforms and fleet operations systems, with a focus on reliability, safety, and over-the-air update infrastructure. The company is hiring reliability engineers, software engineers, and operations specialists to improve vehicle hardware resilience, enhance system automation, and ensure fleet operational excellence.

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Likely interview questions

  • Describe your experience designing and implementing ML pipelines; what challenges did you face and how did you solve them?
  • How have you optimized model inference performance for production systems, and what trade-offs did you consider?
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