Scale Engineer

Software Engineer

Software EngineerL9Hard

Waymo's L9 Software Engineer interview process is designed to assess a candidate's technical expertise, problem-solving abilities, and cultural fit for a leading autonomous driving technology company. The process typically involves multiple rounds, focusing on data structures, algorithms, system design, and behavioral aspects, with an emphasis on real-world application and scalability.

Rounds·4

Timeline·~45d

Experience·5 - 10 yrs

Comp band·US$180000 - US$250000

Interview time·210 min

Evaluation

What they measure.

  • Problem-solving skills: Ability to break down complex problems, identify edge cases, and devise efficient solutions.
  • Technical depth: Strong understanding of computer science fundamentals, algorithms, data structures, and system design principles.
  • Coding proficiency: Ability to write clean, efficient, and well-tested code in relevant programming languages.
  • System design: Ability to design scalable, reliable, and maintainable systems, considering trade-offs.
  • Communication: Clarity in explaining technical concepts, thought processes, and solutions.
  • Collaboration and teamwork: Ability to work effectively with others and contribute to a positive team environment.
  • Adaptability and learning: Willingness to learn new technologies and adapt to changing requirements.
  • Behavioral fit: Alignment with Waymo's values and culture, including a passion for autonomous driving and safety.

Preparation

How to prepare.

Tips

  1. Review fundamental data structures and algorithms, focusing on time and space complexity.
  2. Practice system design problems, considering scalability, reliability, and trade-offs.
  3. Brush up on your preferred programming languages (C++, Python are common at Waymo).
  4. Understand distributed systems concepts (e.g., consensus, fault tolerance, CAP theorem).
  5. Prepare to discuss your past projects and technical challenges in detail.
  6. Research Waymo's mission, values, and recent technological advancements.
  7. Practice behavioral questions using the STAR method (Situation, Task, Action, Result).
  8. Familiarize yourself with common software engineering best practices.

Study plan

Fig · Study plan — 04 phases

01 / 04
01

Phase 01 of 04

Data Structures and Algorithms

Weeks 1-2: Data Structures & Algorithms (LeetCode, HackerRank).

Weeks 1-2: Focus on Data Structures and Algorithms. Cover arrays, linked lists, trees, graphs, hash tables, heaps, sorting, searching, dynamic programming, and graph traversal algorithms. Practice problems on platforms like LeetCode, HackerRank, and Cracking the Coding Interview.

Questions

Commonly asked.

  • Design a system to manage and process sensor data from a fleet of autonomous vehicles.
  • How would you implement a real-time path planning algorithm for a self-driving car?
  • Describe a situation where you had to deal with a critical bug in production. How did you resolve it?
  • What are the trade-offs between different caching strategies in a distributed system?
  • How would you design a system for detecting and avoiding obstacles in real-time?
  • Explain the concept of eventual consistency and when it might be appropriate.
  • Tell me about a time you disagreed with a technical decision. What did you do?
  • How do you ensure the safety and reliability of software in a safety-critical application?
  • Design a distributed key-value store.
  • What are the challenges of scaling a machine learning training pipeline?

Locations

Regional differences.

Fig · Regions — 02 locations

01 / 02

Location

Mountain View, CA

Interview focus

System design for real-time, high-throughput data pipelines.Understanding of distributed systems and their application in autonomous driving.Experience with large-scale data processing and machine learning infrastructure.Problem-solving in safety-critical and performance-sensitive environments.

Common questions

  • How would you design a system to handle real-time sensor data processing for autonomous vehicles?
  • Discuss challenges in scaling distributed systems for autonomous driving.
  • Describe a complex technical problem you solved related to robotics or AI.
  • How do you approach debugging in a safety-critical system?

Tips

  • Familiarize yourself with common cloud platforms (AWS, GCP, Azure) and their services relevant to big data and distributed systems.
  • Study case studies of large-scale systems, particularly in the automotive or robotics industry.
  • Be prepared to discuss your experience with C++, Python, and relevant libraries/frameworks.
  • Highlight any experience with simulation, sensor fusion, or control systems.

Rounds

Round-by-round.

Expand a step for evaluation criteria, sample questions, and prep notes.

DSA

Coding questions at Waymo.

Frequently reported on Waymo loops

View all at Waymo

Other guides

More at Waymo.