# Software Engineer
**Role:** Software Engineer · **Level:** L9
**Company:** [Waymo](https://scaleengineer.com/companies/waymo)
**Difficulty:** Hard
**Salary:** US$180000 - US$250000
**Experience:** 5 - 10
**Timeline:** ~45 days
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.
Canonical: https://scaleengineer.com/interviews/waymo/l9-software-engineer
---
## Overall evaluation

- Technical Skills
- Domain-Specific Knowledge
- Behavioral and Cultural Fit

## Questions 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?

## Preparation tips

### lists

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

### studyPlan

- {"title":"Data Structures and Algorithms","longDescription":"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.","shortDescription":"Weeks 1-2: Data Structures & Algorithms (LeetCode, HackerRank)."}
- {"title":"System Design","longDescription":"Weeks 3-4: Dive into System Design. Study concepts like load balancing, caching, databases (SQL vs. NoSQL), message queues, microservices, and API design. Work through common system design interview questions and case studies.","shortDescription":"Weeks 3-4: System Design (Scalability, Databases, APIs)."}
- {"title":"Distributed Systems and Concurrency","longDescription":"Week 5: Focus on Distributed Systems and Concurrency. Understand concepts like CAP theorem, consensus algorithms (e.g., Paxos, Raft), distributed transactions, and concurrency control mechanisms. Review operating system concepts related to concurrency.","shortDescription":"Week 5: Distributed Systems & Concurrency (CAP Theorem, Consensus)."}
- {"title":"Behavioral and Domain-Specific Preparation","longDescription":"Week 6: Prepare for Behavioral and Domain-Specific Questions. Reflect on your past projects, leadership experiences, and challenges. Research Waymo's work in autonomous driving, AI, and robotics. Practice answering behavioral questions using the STAR method.","shortDescription":"Week 6: Behavioral & Domain Knowledge (Waymo, STAR Method)."}

## Location differences

- {"location":"Mountain View, CA","differences":{"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."],"interviewFocus":["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."],"commonQuestions":["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?"]}}
- {"location":"Detroit, MI","differences":{"tips":["Review common automotive communication protocols (e.g., CAN bus).","Understand the principles of embedded software development and real-time constraints.","Be ready to discuss your experience with C/C++ and low-level programming.","Showcase any projects involving hardware integration or embedded systems."],"interviewFocus":["Software architecture and design patterns for automotive applications.","Understanding of real-time operating systems (RTOS) and embedded systems.","Experience with safety standards and practices in the automotive industry.","Ability to write clean, maintainable, and efficient code for resource-constrained environments."],"commonQuestions":["How would you design a robust API for vehicle control commands?","Discuss trade-offs in choosing between different database technologies for storing vehicle telemetry.","Describe your experience with embedded systems or automotive software development.","How do you ensure code quality and reliability in a production environment?"]}}

## Round 1: Technical Screen - Coding
**Type:** Data Structures and Algorithms · **Difficulty:** Hard · **Duration:** 45 min
Coding interview focusing on data structures and algorithms.
This round focuses on your core computer science fundamentals. You will be presented with coding problems that require you to implement algorithms and data structures. The interviewer will assess your ability to write clean, efficient, and bug-free code, as well as your understanding of time and space complexity. Expect to be asked to explain your thought process and justify your design choices.
**Interviewers look for:** A systematic approach to problem-solving.; Clean, efficient, and correct code.; Understanding of fundamental CS concepts.; Ability to communicate thought process effectively.
**Evaluation criteria:** Problem-solving approach; Algorithmic knowledge; Coding proficiency; Time and space complexity analysis; Edge case handling
**Common rejection reasons:** Inability to articulate thought process clearly.; Lack of fundamental knowledge in data structures or algorithms.; Poor coding practices (e.g., unreadable code, no error handling).; Failure to consider edge cases or optimize for performance.
## Questions

- Given a binary tree, find the lowest common ancestor of two given nodes.
- Implement a function to find the k-th largest element in an unsorted array.
- Design and implement a data structure that supports insert, delete, search, and getRandom in O(1) average time.

## Preparation tips

- Practice coding problems on platforms like LeetCode (Medium/Hard).
- Review common data structures (arrays, linked lists, trees, graphs, hash maps) and algorithms (sorting, searching, dynamic programming, graph traversals).
- Be prepared to explain the time and space complexity of your solutions.
- Practice writing code on a whiteboard or in a shared editor without the aid of an IDE.

## Round 2: System Design Interview
**Type:** System Design · **Difficulty:** Hard · **Duration:** 60 min
Design a scalable and reliable system for a given problem.
This round assesses your ability to design complex, scalable, and reliable systems. You'll be given an open-ended problem (e.g., design a URL shortener, a social media feed, or a system for autonomous vehicle data processing) and expected to discuss various aspects of the design, including data storage, APIs, scalability, and fault tolerance. Focus on clarifying requirements, identifying components, and discussing trade-offs.
**Interviewers look for:** Ability to design complex, large-scale systems.; Understanding of distributed systems and their challenges.; Consideration of various components and their interactions.; Ability to justify design choices and discuss trade-offs.
**Evaluation criteria:** System design principles; Scalability and performance; Reliability and fault tolerance; Trade-off analysis; Clarity of communication
**Common rejection reasons:** Inability to design a scalable and robust system.; Lack of consideration for trade-offs and failure points.; Poor understanding of distributed systems concepts.; Difficulty in communicating complex system designs.
## Questions

- Design a system to handle real-time traffic data for a city.
- Design a distributed cache system.
- How would you design the backend for a ride-sharing service like Uber or Lyft?

## Preparation tips

- Study common system design patterns and architectures.
- Understand concepts like load balancing, caching, database sharding, message queues, and microservices.
- Practice designing systems for scale and high availability.
- Be prepared to discuss trade-offs between different design choices.
- Familiarize yourself with distributed systems concepts.

## Round 3: Hiring Manager Interview
**Type:** Behavioral and Experience Review · **Difficulty:** Medium · **Duration:** 45 min
Discuss past experiences, technical depth, and behavioral fit.
This round is typically conducted by the hiring manager and focuses on your past experiences, technical depth in areas relevant to the team, and behavioral aspects. Be prepared to discuss your resume in detail, highlighting your accomplishments, challenges, and learnings. Behavioral questions will assess your teamwork, leadership, and problem-solving skills in various situations.
**Interviewers look for:** Deep understanding of past projects and technical contributions.; Ability to learn and adapt.; Good communication and collaboration skills.; Alignment with Waymo's values and mission.
**Evaluation criteria:** Past project experience; Technical depth in specific areas; Problem-solving approach; Communication skills; Teamwork and collaboration; Cultural fit
**Common rejection reasons:** Lack of experience in relevant technical areas.; Inability to articulate past experiences and learnings.; Poor communication or interpersonal skills.; Mismatch with Waymo's culture or values.
## Questions

- Tell me about a challenging technical problem you faced and how you solved it.
- Describe a time you had to work with a difficult team member. How did you handle it?
- What are your strengths and weaknesses as an engineer?
- Why are you interested in Waymo and this specific role?

## Preparation tips

- Review your resume and prepare to discuss each project in detail.
- Use the STAR method (Situation, Task, Action, Result) to structure your answers to behavioral questions.
- Think about your strengths, weaknesses, and career aspirations.
- Prepare questions to ask the interviewer about the team, role, and company culture.

## Round 4: Deep Technical / Architectural Interview
**Type:** Advanced Technical / Architectural · **Difficulty:** Hard · **Duration:** 60 min
Deep dive into specialized technical areas or complex architectural problems.
This round often involves senior engineers or leaders who will delve into highly technical or architectural aspects of your experience. It might involve a deep dive into a specific project, a complex system design problem, or a discussion on a specialized technical domain relevant to Waymo's work. The goal is to assess your ability to operate at a high technical level and drive significant impact.
**Interviewers look for:** Deep expertise in areas relevant to Waymo's technology (e.g., AI, ML, robotics, distributed systems).; Ability to tackle highly complex and ambiguous problems.; Strong architectural vision and understanding of trade-offs.; Evidence of significant impact and ownership in previous roles.
**Evaluation criteria:** Deep technical expertise; Problem-solving in specialized domains; Architectural thinking; Innovation and creativity; Impact and ownership
**Common rejection reasons:** Lack of deep technical expertise in a specific domain.; Inability to handle complex, ambiguous problems.; Poor communication of technical ideas.; Not demonstrating a high level of ownership or impact.
## Questions

- Discuss the architecture of a large-scale machine learning system you designed or contributed to.
- How would you design a system for real-time object detection and tracking in autonomous vehicles?
- Describe a time you had to make a significant technical trade-off. What was the outcome?

## Preparation tips

- Identify areas of deep expertise relevant to Waymo (e.g., machine learning, computer vision, robotics, distributed systems, high-performance computing).
- Prepare to discuss complex technical challenges and your innovative solutions.
- Think about the architectural decisions you've made and their impact.
- Be ready to discuss cutting-edge technologies and research in relevant fields.