Waymo Software Engineer Interview Guide (2025) – Coding, System Design & Salary Insights

The path to becoming a Waymo software engineer is both challenging and rewarding. You are entering a domain where real-time systems, AI, and large-scale infrastructure converge to power fully autonomous driving. Waymo, an Alphabet subsidiary, has already clocked over 100 million autonomous miles and continues to scale rapidly. With 250,000+ paid rides every week and over 700,000 monthly trips in California alone, this is no ordinary engineering environment. It demands precision, creativity, and robust systems thinking. If you’re preparing for a Waymo SWE interview in 2025, you need to be ready for more than just algorithmic questions. You’ll face system design focused on safety and autonomy, machine learning assessments, and behavioral scenarios where cross-functional alignment is key. This guide will walk you through it all.

Role Overview & Culture

Your responsibilities as a Waymo SWE revolve around building systems that make autonomy work at scale. You might be refining LiDAR fusion algorithms in C++ or optimizing Python-based simulation frameworks that test edge-case behaviors billions of times. Daily, you will collaborate with hardware and robotics teams, contribute to highly scrutinized code reviews, and analyze real-world driving incidents to improve safety logic.

This is not theoretical work—it is production-level code that controls 3,000+ vehicles across five major cities. The culture at Waymo is as dynamic as the domain. You will be expected to take full ownership of your systems, iterate quickly, and learn constantly. Simulation competitions and internal deep dives keep innovation alive. At its core, Waymo’s engineering culture is about teamwork, responsibility, and safety as a non-negotiable priority.

Why This Role at Waymo?

Working at Waymo puts you at the heart of one of the most safety-critical and technically advanced domains in modern engineering. You will be designing systems that, in real-world deployment, have already reduced bodily injury claims by over 92% compared to human drivers. That stat isn’t just impressive—it means the code you write can save lives.

At the same time, you will leverage Alphabet’s infrastructure, processing petabytes of driving, simulation, and sensor data in real time. Waymo doesn’t wait for annual releases. Updates go live on a fleet that operates daily across major U.S. cities. You will watch your contributions roll out to actual vehicles within weeks. If you care about research-to-production impact, real-world scalability, and engineering that matters, this is the kind of challenge where your skills will thrive and grow.

The Waymo software engineer interview process is a rigorous five-stage evaluation designed to assess your technical skill, communication clarity, and cultural fit within a safety-critical engineering environment. Each stage focuses on a specific set of competencies to ensure you are well-suited for Waymo’s mission and pace:

• Application Submission
• Recruiter Screen
• Technical/Phone Screen
• Full‑loop On‑site
• Hiring Committee & Offer

Application Submission

Your journey begins with the application stage, which often determines if you even get a first conversation. Waymo receives thousands of applications each month. To stand out, your resume must demonstrate robust fundamentals in C++ or Python, since these languages power the majority of Waymo’s perception and planning stacks. Experience with large-scale data pipelines, ML inference workflows, or simulation tooling is a plus, especially given Waymo processes petabytes of sensor data weekly. Candidates who highlight concrete impact—such as system latency reduction, model accuracy improvements, or simulation runtime scaling—tend to advance. A strong application shows that you are not just technically sound but that your experience aligns with the scale and safety-critical focus of Waymo’s engineering environment.

Recruiter Screen

The recruiter screen is a 15 to 30-minute exploratory call that focuses on you and your motivations. This is not a coding round, but it is your first real opportunity to showcase your alignment with Waymo’s mission. You will discuss your background, projects, and technical strengths. You will also get a chance to express which teams—such as infrastructure, planning, or perception—best match your interests. Given that Waymo’s vehicle platform runs ²⁴⁄₇ in real cities, recruiters are especially interested in candidates who can work cross-functionally and understand the gravity of real-world deployment. Be prepared to discuss compensation expectations, work authorization, and relocation or remote preferences. Treat this call as a two-way alignment checkpoint, not just a gate.

Technical / Phone Screen

Next comes the technical screen, typically consisting of one or two 45-minute remote interviews. You will solve algorithmic problems involving graph traversal, dynamic programming, or tree manipulation. Interviewers want to see structured problem solving, not brute force coding. Expect to code in C++ or Python on a collaborative editor while talking through your reasoning.

In some cases, domain-specific problems like real-time sensor data parsing may appear, especially if your background involves perception or embedded systems. Communication is key. A clean solution that you explain well is more valuable than cleverness without clarity. These screens simulate on-the-job debugging and algorithm implementation, so aim to reflect how you would work on production-critical systems at Waymo.

Full‑Loop Onsite

If you advance to the on-site stage, you will face a sequence of four to five in-depth interviews, each targeting a distinct capability. You will write advanced algorithms, such as Dijkstra variants or recursive dynamic programming, and architect distributed systems for simulation or fleet telemetry. For roles tied to ML, you might analyze perception model pipelines or propose optimization strategies for real-time inference.

One interview evaluates your collaboration style and ethical decision-making, critical when shipping software for vehicles operating without a driver. In some cases, you will speak briefly with potential teammates to explore team fit. The expectations here are high: you should code fluently, design with scalability and safety in mind, and communicate your thinking with confidence and humility.

Hiring Committee & Offer

After your interviews, each interviewer submits written feedback, which the hiring committee reviews independently. This ensures a fair, consistent evaluation across teams and locations. The committee weighs your coding quality, system design fluency, and behavioral alignment. Strong performance across multiple areas can offset one weaker round. If successful, you will receive an offer package including compensation, equity, and benefits. You will also have a debrief call to clarify any final questions.

Waymo’s compensation is competitive within Alphabet, and equity grants reflect the company’s long-term autonomy roadmap. If you do not pass, you may still receive encouraging closure with possible reapplication timelines. Every step reflects Waymo’s deep investment in finding engineers who can drive technical innovation safely and collaboratively.

Waymo interviews are known for their technical rigor and cross-functional depth, so understanding the types of questions you’ll face is essential to prepare confidently and thoroughly.

Coding / Technical Questions

Expect Waymo coding interview questions that emphasize algorithmic problem-solving, data structures, and real-time thinking:

1. Write a query to get the average commute time for each commuter in New York

To solve this, use two subqueries: one to calculate the average commute time for each commuter in New York grouped by commuter_id, and another to calculate the overall average commute time across all commuters in New York. Use the TIMESTAMPDIFF function to calculate the duration of each ride in minutes, and then join the results to display both averages in the output.

2. Find the average number of accepted friend requests for each age group

To solve this, use a RIGHT JOIN between the requests_accepted and age_groups tables to associate accepted friend requests with age groups. Then, calculate the average acceptance rate by dividing the count of accepted requests by the count of unique users in each age group, grouping by age group, and ordering the results in descending order.

3. Calculate daily sales of each product since last restocking

To solve this, use a Common Table Expression (CTE) to find the latest restocking date for each product using the MAX() function. Then, join the sales table with the CTE and the products table, filtering sales that occurred after the last restocking date. Use a window function SUM(...) OVER() to calculate the running total of sales for each product since its last restocking.

4. Given a list of integers, find the index at which the sum of the left half of the list is equal to the right half.

To solve this, calculate the total sum of the list and iterate through the list while maintaining a running sum of the left side. For each index, compute the right side sum using the formula total - leftsum - current_element. If the left and right sums match, return the index; otherwise, return -1 after completing the iteration.

5. Implementing the Fibonacci Sequence in Three Different Methods

To solve this, implement the Fibonacci sequence using three methods: recursive, iterative, and memoization. The recursive method calls itself repeatedly, the iterative method uses a loop, and memoization stores results of previous calculations to avoid redundant computations. Each method has different time complexities: recursive (O(2^n)), iterative (O(n)), and memoization (O(n)).

6. Write a Python function called max_profit that takes a list of integers, where the i-th integer represents the price of a given stock on day i, and returns the maximum profit you can achieve by buying and selling the stock.

To solve this, iterate through the list of prices while maintaining four variables: buy1, profit1, buy2, and profit2. These variables track the lowest prices and maximum profits for two transactions. By updating these values dynamically, the function calculates the maximum profit achievable with at most two buy/sell transactions.

System Design Questions

In a Waymo system design interview, you’ll be asked to architect scalable, latency-aware systems that support safety-critical applications. You should be able to explain trade-offs in autonomy stack design, build fault-tolerant data pipelines, and describe infrastructure that supports petabyte-scale sensor data and billions of simulation miles:

7. Design a data warehouse for a new online retailer

To design a data warehouse for an online retailer, start by identifying the business process, such as sales data for analytics. Define the granularity of events, identify dimensions like buyer details, item specifics, and transaction time, and determine facts like quantity sold and revenue. Finally, create a star schema with a central fact table and dimension tables for efficient querying.

8. Design a data pipeline for hourly user analytics

To build this pipeline, you can use SQL queries to aggregate data for hourly, daily, and weekly active users. A naive approach involves querying the data lake directly for each dashboard refresh, while a more efficient solution aggregates and stores the data in a reporting table or view, updated hourly using an orchestrator like Airflow. Edge cases like delayed data can be handled by setting cut-off thresholds.

9. Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner’s partners

To architect the ETL pipeline, use a modular design with components like orchestration layers, partner connectors, message queues, schema registries, transformation workers, and storage layers. This ensures scalability, fault tolerance, and high data freshness while accommodating diverse partner APIs and schemas. The system should also include monitoring, lineage tracking, and retry mechanisms for reliability.

10. How would you collect and aggregate data for multimedia information, specifically when it’s unstructured data from videos?

To collect and aggregate unstructured video data, start with primary metadata collection and indexing, which involves automating the extraction of basic metadata like author, location, and format. Next, use user-generated content tagging, either manually or scaled with machine learning, to enrich the dataset. Finally, perform binary-level collection for detailed analysis, such as color and audio properties, while considering cost-effectiveness. Automated content analysis using machine learning techniques like image recognition and NLP can further enhance the process.

11. Assume you are receiving CSV files intermittently utilizing an SFTP. Design a pipeline to make this data usable.

To design an SFTP pipeline, start by setting up an SFTP connection using libraries like paramiko in Python. Retrieve and parse files based on their type (e.g., CSV) and validate the data. Store the processed data in a cloud database like Snowflake, ensuring error handling, logging, and secure credential management. Extend the pipeline to handle other file formats and databases as needed.

Behavioral / Culture‑Fit Questions

Waymo behavioral interview questions explore how you work in high-stakes, collaborative environments where safety and ownership are non-negotiable. Be ready to discuss ethical decisions, failure recovery, stakeholder communication, and how your past actions reflect Waymo’s mission of building safe, autonomous systems:

12. What do you tell an interviewer when they ask you what your strengths and weaknesses are?

Waymo expects engineers who are self-aware and continuously improving, especially in a safety-critical domain. When discussing strengths, choose qualities that directly support mission ownership, such as your ability to lead technical reviews or build fault-tolerant systems under pressure. For weaknesses, show maturity by identifying a real area for growth, then describe how you’ve actively worked to improve it in ways that align with Waymo’s high standards of quality and collaboration.

13. Why Do You Want to Work With Us

This question gives you a chance to demonstrate alignment with Waymo’s mission to eliminate human driving error through autonomous technology. Mention specific aspects of the company—such as its 100M+ autonomous miles driven or ML pipeline scale—that resonate with your career goals or values. Your answer should connect your technical interests and ethical mindset to the real-world impact Waymo is making in safety, mobility, and AI innovation.

14. How would you convey insights and the methods you use to a non-technical audience?

At Waymo, effective cross-functional communication is essential, as you may collaborate with policy teams, UX designers, or safety operations staff. Explain how you translate technical concepts like perception confidence scores or system latency into business- or safety-relevant terms. Use a past example to show that you not only understand the tech but also tailor your messaging for clear, actionable decision-making outside of engineering.

15. Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?

In autonomous vehicle development, aligning with non-technical stakeholders is critical, especially when decisions affect safety or public deployment. Share a scenario where communication initially failed, such as a misunderstood design trade-off or missed expectation, and how you adapted, perhaps by reframing technical details or using data visualizations. Emphasize how the experience improved your communication and strengthened cross-functional trust moving forward.

Interviewing at Waymo in 2025 means proving your skills in high-stakes, real-world systems. As a mid-senior engineer, you’ll face challenges in algorithms, system design, and AV-focused problem solving. Strong preparation is key. The tips below reflect current trends and expectations to help you stand out and succeed.

Master LeetCode-style DSA

Waymo’s software engineering interviews still heavily focus on algorithms and data structures. To succeed, immerse yourself in coding practice. Mid-senior candidates should confidently solve medium-difficulty problems within tight time limits. Focus on core topics like graph traversal for pathfinding, dynamic programming for optimization, and efficient data manipulation.

In 2025, interviewers expect not just the correct solution but also clear explanations and optimal complexity. Simulate real interview conditions by coding on a whiteboard or shared editor without autocomplete. Study patterns from Waymo LeetCode challenges reported by others, and analyze solutions for trade-offs. Make sure you can justify your choices and handle edge cases gracefully. Mastery of DSA fundamentals ensures you can tackle any coding challenge thrown your way at Waymo.

Rehearse system-design frameworks

At a mid-senior level, you’ll likely face system design questions evaluating how you architect complex systems. Develop a structured approach to tackle a Waymo system design interview with confidence. Begin by clarifying requirements and constraints (scale, latency, safety) for the given problem. Outline a high-level architecture covering key components – for example, data ingestion from vehicles, real-time processing pipelines, storage, and APIs for monitoring. Discuss choices like databases, messaging systems, and failover mechanisms, tying them to Waymo’s autonomous driving context.

In 2025, showcase awareness of modern, scalable design patterns (like microservices or cloud-native designs). Practicing frameworks for system design ensures you can organize your thoughts and cover edge cases under pressure.

Mock interviews & timing

Practice makes perfect when preparing for Waymo’s rigorous interview process, even for seasoned developers. Conduct mock coding interviews using realistic scenarios and time limits to build confidence. Use common Waymo coding interview questions from forums or practice sites to get familiar with the question style. Simulate a live interview environment – code on a blank screen or whiteboard while explaining your logic out loud. Focus on timing: for each problem, allocate time to understand it, outline an approach, code the solution, and test edge cases.

In 2025, many interviews are virtual, so get comfortable with online coding platforms and video calls. Regularly timed practice helps improve your problem-solving speed and communication under interview pressure.

Study AV domain papers

Waymo operates at the cutting edge of autonomous vehicle technology, so demonstrating domain insight can set you apart. Deepen your understanding of the autonomous driving (AV) field by reading recent research papers, technical blogs, and Waymo’s own publications. Focus on topics like sensor fusion, computer vision for perception, route planning algorithms, and safety systems.

As of 2025, the AV industry is rapidly evolving—new machine learning techniques and improved LIDAR/radar sensors are regularly emerging. By mentioning relevant industry developments or lessons from Waymo’s open-source data and research, you show interviewers that you’re passionate and informed. This context can enrich your answers in system design or behavioral discussions, highlighting you as a well-rounded candidate.

STAR stories for culture fit

Beyond technical skills, Waymo looks for engineers who embody its safety-first and innovative culture. Prepare for the behavioral portion by crafting several STAR stories (Situation, Task, Action, Result) from your past experiences. Expect typical Waymo behavioral interview questions exploring teamwork, dealing with setbacks, leadership, and how you prioritize safety and quality. For each story, clearly outline the challenge you faced and how you solved it while collaborating effectively. Emphasize outcomes that align with Waymo’s values—perhaps you improved a process to prevent errors or led an initiative that required innovation and caution.

In 2025, as Waymo scales its deployments, it values adaptable team players. Well-structured STAR examples will demonstrate your cultural fit and communication skills.

What salary can I expect at Waymo as an SWE?

How long is the hiring process?

The Waymo interview timeline typically spans 4 to 7 weeks from application to offer. Timing depends on team needs, scheduling availability, and candidate readiness for each round. After submitting your application, expect a recruiter screen within one to two weeks. Technical interviews may follow shortly after. Full-loop interviews and hiring committee reviews take additional time, especially if feedback windows or bar-raiser input is involved. If you are interviewing for senior or niche roles, the process may take slightly longer due to deeper system design evaluations or extended team-matching discussions.

Who reaches out first?

The Waymo recruiter is usually the first person to contact you after your application is reviewed. They handle the initial screen, alignment discussion, and overall process navigation. If you advance beyond the early stages, a recruiting coordinator will often step in to schedule interviews and ensure logistics are smooth. While recruiters focus on matching your experience with the right team, coordinators manage calendars and communication around technical rounds. Both play a key role in helping you move through Waymo’s multi-stage hiring process efficiently and with clarity.

Is Waymo hiring right now?

Yes, the answer is: Waymo is hiring in 2025. Following rapid fleet expansion across Phoenix, San Francisco, Austin, Los Angeles, and Atlanta, Waymo continues to grow its engineering teams. With over 250,000 weekly paid rides and 100 million autonomous miles logged, the company is scaling both infrastructure and perception systems. Hiring priorities include software engineers in simulation, safety analytics, machine learning, and planning. Waymo’s current headcount outlook reflects Alphabet’s long-term investment in autonomy, making it a prime time to apply if your skills align with real-time systems and scalable AI development.

How to get a job at Waymo?

If you’re wondering how to get a job at Waymo, your first step is understanding the five-stage hiring process covered earlier. Success depends on more than just strong coding. You’ll need to show deep systems thinking, safety-first decision making, and the ability to collaborate across functions. Start by submitting a tailored application that highlights impact, then prepare for interviews that cover algorithms, system design, and behavioral fit. Use the tips in the guide above to target your preparation. Next, apply for the current job openings. With the right approach and mindset, you can navigate Waymo’s rigorous process and join one of the most advanced teams in autonomous mobility.

Preparing for a Waymo software engineer interview can feel intense, but with focus and the right strategy, you’ll walk in confident and ready. From algorithms to system design to culture-fit conversations, every round rewards clarity, preparation, and technical depth. Remember, even at a mid-senior level, success comes down to structured practice and thoughtful communication. Whether you’re reviewing LeetCode or refining STAR stories, commit to consistency. Try a free mock interview and dive into the Interview Query Waymo SQL question bank to sharpen your skills. For a complete walkthrough, revisit the Waymo Interview Questions & Process or explore guides for other roles like Data Scientist and ML Engineer. Good luck!