Application Guide

How to Apply for Senior/Staff Software Engineer, Machine Learning Infrastructure

at Nuro

🏢 About Nuro

Nuro is pioneering autonomous delivery robots that are electric, efficient, and designed to reduce emissions, offering a tangible solution for sustainable last-mile logistics. Working here means contributing directly to cutting-edge robotics and AI that impacts real-world transportation and commerce, with a mission-driven focus on environmental sustainability.

About This Role

This role focuses on building and maintaining the machine learning infrastructure that powers Nuro's autonomous delivery robots, including tools for model development, deployment, and monitoring. You'll ensure high reliability of ML services and enable robust experimentation, directly impacting the safety and efficiency of Nuro's robotic fleet.

💡 A Day in the Life

A typical day might involve designing a new experiment tracking tool to streamline model development, debugging a performance issue in a distributed ML service to maintain fleet uptime, and collaborating with perception teams to integrate infrastructure updates that enhance robot navigation reliability. You'll also review observability dashboards to monitor model metrics and ensure data quality across deployments.

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Interview Prep

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🎯 Who Nuro Is Looking For

  • Has 2+ years of experience in ML infrastructure, with hands-on work on feature stores, experiment tracking, or model registries (bonus if with open-source tools like MLflow or Feast)
  • Strong Python/C++ coder who has profiled and optimized performance bottlenecks in distributed systems relevant to ML workloads
  • Demonstrates a proactive, problem-solving mindset to overcome obstacles and accelerate development for user-facing ML systems
  • Understands the full ML development lifecycle and can champion best practices for reproducibility and debuggability in a production robotics environment

📝 Tips for Applying to Nuro

1

Highlight specific projects where you built or maintained tools for the ML lifecycle (e.g., feature store, experiment tracking) and quantify their impact on model development speed or reliability

2

Emphasize any experience with performance optimization in distributed systems, especially related to ML inference or training pipelines

3

Tailor your resume to mention robotics, autonomous systems, or real-time ML applications, as Nuro's context is critical

4

If you have open-source ML infrastructure experience, detail your contributions or usage of tools like MLflow, Kubeflow, or Feast

5

Showcase collaboration with cross-functional teams (e.g., ML researchers, engineers) to align infrastructure with domain needs, as Nuro values integration across robotics and AI

✉️ What to Emphasize in Your Cover Letter

["Express passion for Nuro's mission of sustainable delivery via autonomous robots and how ML infrastructure enables that vision", 'Detail a specific example of building or improving an ML tool (e.g., for deployment, monitoring) that increased system uptime or developer productivity', 'Explain your approach to ensuring robust, reproducible ML experimentation in a fast-paced, safety-critical environment like robotics', 'Highlight experience with performance optimization in distributed systems, tying it to real-world ML applications']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Explore Nuro's blog, press releases, or technical papers to understand their ML stack and robotics challenges (e.g., perception, navigation)
  • → Learn about Nuro's partnerships and deployments (e.g., with Domino's, FedEx) to grasp real-world use cases for their ML infrastructure
  • → Investigate the autonomous vehicle or robotics industry trends, especially around ML reliability and safety standards
  • → Review open-source ML tools Nuro might use or contribute to, based on the job description's bonus points

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Designing a feature store or model registry for a scalable ML pipeline in an autonomous vehicle context
2 Strategies for detecting issues and alerting in critical ML services to ensure high uptime for real-time robotics
3 Optimizing performance bottlenecks in distributed ML systems, possibly with Python/C++ profiling examples
4 Implementing observability dashboards to track model performance, data quality, and metrics in a production setting
5 Collaborating with cross-functional teams (e.g., perception, planning) to integrate ML infrastructure solutions across robotics domains
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⚠️ Common Mistakes to Avoid

  • Applying with a generic ML engineer resume without highlighting infrastructure or tool-building experience specific to the ML lifecycle
  • Failing to demonstrate understanding of distributed systems performance optimization or real-time ML applications in interviews
  • Neglecting to research Nuro's mission and robotics focus, leading to misaligned answers on sustainability or autonomous systems impact

📅 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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