Application Guide

How to Apply for Senior Software Engineer, ML Core

at Zoox

🏢 About Zoox

Zoox is pioneering fully autonomous, purpose-built electric vehicles designed specifically for dense urban environments, not retrofitting existing cars. Their unique approach integrates the vehicle, AI, and mobility service into a single cohesive system aimed at eliminating traffic congestion and reducing carbon emissions. Working here means contributing to a vertically integrated solution that could fundamentally transform urban transportation.

About This Role

This Senior Software Engineer role focuses on building the core ML infrastructure and tooling that enables Zoox's autonomous vehicle AI to be developed, trained, and deployed efficiently. You'll directly impact model inference latency on vehicles and improve developer productivity across ML teams, making you a critical enabler of their real-world autonomous driving system. Your work bridges applied research and production deployment in a safety-critical domain.

💡 A Day in the Life

A typical day might involve optimizing a PyTorch model's TensorRT conversion to shave milliseconds off inference latency, collaborating with hardware engineers on GPU utilization patterns, and developing internal tools that help ML researchers debug training issues more efficiently. You'll balance deep technical work on performance optimization with cross-team coordination to ensure library upgrades don't break production vehicle systems.

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Interview Prep

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🎯 Who Zoox Is Looking For

  • Has 6+ years building production software systems, with deep experience in Python or C++ for performance-critical applications
  • Demonstrates hands-on experience with ML training frameworks (PyTorch, JAX, etc.) and GPU-accelerated inference toolchains (CUDA, TensorRT) for optimizing model deployment
  • Possesses experience building developer tools, libraries, or platforms that improve ML workflow efficiency and debugging capabilities
  • Shows ability to collaborate across research, hardware, and infrastructure teams to solve complex systems integration challenges

📝 Tips for Applying to Zoox

1

Highlight specific examples where you've optimized ML inference latency, especially with TensorRT or similar tools for edge/vehicle deployment

2

Demonstrate experience with managing foundational ML library upgrades (PyTorch versions, CUDA compatibility) in production environments

3

Showcase projects where you built tooling that improved ML developer experience or debugging efficiency for teams

4

Emphasize any experience with safety-critical systems, real-time constraints, or autonomous vehicle/robotics domains

5

Quantify impact: mention percentage improvements in training time, inference latency reductions, or developer productivity gains from your past work

✉️ What to Emphasize in Your Cover Letter

['Explain your experience with ML infrastructure at scale, particularly for real-time or edge deployment scenarios', 'Describe your approach to balancing innovation with stability when managing foundational libraries in production ML systems', 'Highlight cross-functional collaboration experience, especially bridging research and engineering teams', "Express specific interest in Zoox's integrated approach to autonomous vehicles and how your skills align with their unique challenges"]

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Zoox's unique vehicle design and integrated AI approach (different from competitors like Waymo or Cruise)
  • → Their technical blog posts and research publications about ML infrastructure and model deployment
  • → Recent news about their testing, deployment progress, or partnerships in specific cities
  • → Their technology stack mentions in engineering talks or conference presentations

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Deep dive into your experience optimizing PyTorch models with TensorRT for latency-critical applications
2 System design question about building a library/tooling system for managing ML dependencies across a large organization
3 Scenario-based question about debugging a performance regression in on-vehicle model inference
4 Discussion of trade-offs between custom ML solutions versus off-the-shelf libraries for autonomous vehicle applications
5 Cross-functional collaboration scenario involving alignment between research teams and hardware engineers
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⚠️ Common Mistakes to Avoid

  • Focusing only on ML modeling/research experience without demonstrating production engineering and tooling skills
  • Generic statements about interest in AI/autonomous vehicles without showing understanding of Zoox's specific approach
  • Failing to demonstrate experience with the specific technologies mentioned (TensorRT, PyTorch, CUDA) in production contexts

📅 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 Zoox!

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