Application Guide

How to Apply for Software Engineer, Machine Learning Infrastructure

at Nuro

🏢 About Nuro

Nuro is pioneering autonomous delivery robots that are transforming local commerce while reducing emissions and traffic congestion. Their focus on electric, efficient delivery solutions for everyday goods makes them unique in the robotics space, combining cutting-edge AI with tangible environmental impact. Working at Nuro means contributing to sustainable urban mobility solutions that directly benefit communities.

About This Role

This Software Engineer role focuses on building and maintaining the machine learning infrastructure that powers Nuro's autonomous delivery robots. You'll be responsible for creating tools that track the entire ML development lifecycle—from feature stores and experiment tracking to model deployment and performance monitoring—ensuring robust, reproducible ML systems that directly impact vehicle safety and reliability.

💡 A Day in the Life

A typical day involves collaborating with ML researchers to understand their infrastructure needs, developing tools for experiment tracking and model management, maintaining observability dashboards to monitor model performance in production robots, and implementing improvements to deployment pipelines. You'll balance building new infrastructure features with ensuring existing ML services maintain high uptime for Nuro's delivery operations.

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Interview Prep

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🎯 Who Nuro Is Looking For

  • Has 2+ years experience building ML infrastructure tools (feature stores, experiment tracking, model registries) in Python, with exposure to C++ being a plus for performance-critical components
  • Demonstrates hands-on experience profiling and optimizing performance bottlenecks in distributed ML systems, not just theoretical knowledge
  • Shows practical understanding of the entire ML development lifecycle through projects or work experience, particularly around deployment and monitoring
  • Exhibits a problem-solving mindset focused on overcoming obstacles to make systems work better for end-users, aligning with Nuro's mission-driven culture

📝 Tips for Applying to Nuro

1

Highlight specific experience with ML infrastructure tools like MLflow, Kubeflow, Feast, or similar platforms in your resume, not just ML model development

2

Prepare concrete examples of how you've optimized performance bottlenecks in distributed systems, quantifying improvements where possible

3

Research Nuro's technical blog and publications to understand their ML stack and reference specific technologies they use in your application

4

Demonstrate understanding of both ML development lifecycle AND infrastructure reliability needs—this role bridges both domains

5

Show how your experience aligns with Nuro's mission by connecting technical work to real-world impact in sustainable delivery

✉️ What to Emphasize in Your Cover Letter

['Explain your experience with ML infrastructure tools for tracking model development lifecycle (feature stores, experiment tracking, model registries)', "Describe specific instances where you've implemented observability dashboards or alerting mechanisms for ML services", "Connect your technical skills to Nuro's mission of reliable autonomous delivery and sustainable transportation", 'Highlight collaboration experience with cross-functional teams to identify and solve infrastructure needs']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Study Nuro's technical blog posts about their ML infrastructure and autonomy stack
  • → Research their specific delivery use cases and how ML models power different aspects of their robots
  • → Understand their deployment scale and geographic operations to appreciate system reliability requirements
  • → Review their company values and mission to align your application with their sustainability focus

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Designing a feature store for autonomous vehicle sensor data with considerations for latency and reliability
2 Implementing experiment tracking for ML models in safety-critical systems like autonomous driving
3 Building alerting mechanisms for model performance degradation in production ML services
4 Optimizing distributed training or inference pipelines for real-time autonomous systems
5 Collaborating with perception, prediction, and planning teams to understand their infrastructure needs
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⚠️ Common Mistakes to Avoid

  • Focusing only on ML model development without demonstrating infrastructure or tooling experience
  • Being unable to discuss specific performance optimization examples in distributed systems
  • Showing limited understanding of the full ML lifecycle beyond training and deployment
  • Failing to connect technical skills to Nuro's mission of reliable, sustainable delivery

📅 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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