Application Guide

How to Apply for Software Engineer, ML Platform Infrastructure

at Nuro

🏢 About Nuro

Nuro is a self-driving technology company with a mission to make autonomy accessible to all. What sets Nuro apart is its focus on practical, scalable autonomy through the Nuro Driver™, which can be licensed for various applications from robotaxis to personal vehicles. Working at Nuro means contributing to a future of safer, more efficient transportation while reducing emissions.

About This Role

As a Software Engineer on the ML Platform Infrastructure team, you will build and evolve the core platform that provides seamless access to compute and data resources for researchers and engineers. Your work will directly accelerate the development lifecycle of Nuro's autonomous driving technology, from experimentation to production.

💡 A Day in the Life

A typical day might start with a standup with the ML Platform team to discuss progress on resource provisioning automation. You might then dive into coding a new Kubernetes operator for GPU scheduling, followed by a code review for a colleague's IaC changes. In the afternoon, you could troubleshoot a data pipeline latency issue and collaborate with ML researchers to optimize their training workflows.

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Interview Prep

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🎯 Who Nuro Is Looking For

  • Has deep expertise in large-scale infrastructure, workload orchestration (e.g., Kubernetes, Slurm), and data processing pipelines.
  • Is experienced with Infrastructure as Code (IaC) tools like Terraform and can automate resource provisioning in cloud environments (AWS, GCP).
  • Understands the unique demands of ML workflows, including GPU scheduling, distributed training, and feature management.
  • Possesses strong software engineering skills in Python or Go, and is comfortable working in a fast-paced, collaborative environment.

📝 Tips for Applying to Nuro

1

Tailor your resume to highlight specific projects where you built or scaled infrastructure for ML workloads, emphasizing metrics like reduced job wait times or improved resource utilization.

2

In your cover letter, explicitly connect your experience with workload orchestration (e.g., Kubernetes) to Nuro's need for efficient scheduling of ML experiments.

3

Demonstrate familiarity with Nuro's technology by mentioning the Nuro Driver™ and how your infrastructure work could accelerate its development.

4

If possible, showcase open-source contributions to relevant projects (e.g., Kubernetes operators, ML workflow tools) as evidence of your expertise.

5

Prepare to discuss trade-offs between different orchestration solutions (e.g., Kubernetes vs. Slurm) and how you've made those decisions in past roles.

✉️ What to Emphasize in Your Cover Letter

['Emphasize your experience building scalable ML infrastructure that directly impacted model development speed.', 'Highlight your ability to automate resource provisioning and manage large-scale clusters, as this is core to the role.', "Show passion for autonomous driving and Nuro's mission to make autonomy accessible and sustainable.", 'Mention specific technical skills like Terraform, Kubernetes, and Python/Go that align with the job requirements.']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Read about Nuro's autonomous driving technology, especially the Nuro Driver™ and its applications in robotaxis and delivery.
  • → Look into Nuro's engineering blog or talks to understand their tech stack and infrastructure challenges.
  • → Research Nuro's partnerships and commercial deployments to grasp the scale and impact of their work.
  • → Understand the competitive landscape of autonomous driving and where Nuro positions itself (e.g., focus on scalability vs. full autonomy).

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Design a system for automated resource provisioning for ML training jobs across a hybrid cloud environment.
2 How would you optimize GPU scheduling to minimize idle time while ensuring fair access for multiple teams?
3 Describe a time you debugged a performance bottleneck in a distributed training pipeline.
4 How would you implement a feature store for ML models in the context of autonomous driving?
5 Discuss the trade-offs between using Kubernetes and Slurm for workload orchestration in an ML platform.
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⚠️ Common Mistakes to Avoid

  • Don't focus solely on ML model development; this role is about infrastructure, so highlight systems and platform work.
  • Avoid generic statements about wanting to work in AI without connecting to Nuro's specific mission and technology.
  • Don't neglect to show experience with production-grade systems; emphasize reliability, scalability, and automation.

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to Nuro!

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