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# Autonomy ML Infrastructure

- **Company:** Nuro Inc.
- **Location:** United States
- **Experience:** Expert
- **Salary:** $193,930.0
- **Contract:** Permanent contract
- **Skills:** C++ (Programming Language), Compilers, Profiling, Nvidia CUDA, Python (Programming Language), Machine Learning, Tensorflow, Pytorch, Large Language Models, Deep Learning, Keras, Build Management, Optimization Algorithms, Machine Learning Operations
- **Published:** June 23, 2026
- **Apply:** https://www.dice.com/job-detail/a1f759be-3a15-4090-80c8-ce20c6db442e

## About the Role

* 3+ years of relevant experience in ML optimization infrastructure.
 * Experience with ML optimization techniques such as quantization and pruning, and ML compilers.
 * Experience maintaining, profiling, and optimizing GPU ML compilers & runtimes.
 * Proficient in Python and working experience with C++ and CUDA.
 * Working experience deep learning frameworks (like PyTorch, Jax, Tensorflow, Keras).
 * Proficient in Python and working experience with C++.
 * You are passionate about accelerating the benefits of robotics for everyday life.

## Description

The Autonomy ML Infrastructure team is responsible for building & improving the core infrastructure for autonomy teams at Nuro. In this role, you will work closely with teams across Nuro, to design, build and deploy core infrastructure components in machine learning model life cycle, to push the autonomous future forward. You will have an opportunity to work across the full stack of machine learning solutions - from designing robust and scalable model pipelines to building to deploying the optimized models on Nuro's fleet of self-driving robots!

About the Work

 * Optimize Nuro's autonomy stack with cutting-edge optimization techniques like quantization, distillation, and model compression.
 * Work with autonomy engineers to optimize, validate, and deploy large language models.
 * Develop and maintain a world-class model compiler framework, FTL.
 * Write robust, high quality software to increase our confidence in our vehicle's ability to navigate safely on-road.
 * Collaborate closely with machine learning domain experts and engineers across behavior, perception and mapping to design and implement end-to-end learned ML solutions.


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