Staff Software Engineer, Autonomy Evaluation Interview Experience at Cruise

I recently interviewed for the Staff Software Engineer, Autonomy Evaluation position at Cruise and would like to share my experience to help others prepare for the interview process. This role is focused on evaluating and improving the performance of autonomous vehicles (AVs) through data analysis, simulation, and testing. The goal is to ensure that the AV stack operates safely, efficiently, and reliably in real-world environments.

Overview of the Role

As a Staff Software Engineer, Autonomy Evaluation, you will work on developing, monitoring, and analyzing metrics for AV systems, particularly in the context of their driving capabilities. You’ll collaborate closely with AV engineers, data scientists, and product teams to define success criteria, run evaluations, and optimize the system’s performance. This role combines deep technical expertise in machine learning, data analysis, and AV systems, with a strong emphasis on leadership and mentoring.

Interview Process

The interview process was rigorous, involving multiple rounds to assess both technical skills and leadership abilities. Here’s an overview of the steps I went through:

1. Initial Screening (HR Interview)

Overview: The first step was an initial conversation with an HR recruiter. They asked general questions about my background, motivation for applying, and logistical details such as salary expectations and remote work preferences.
Example Question: “What excites you about working at Cruise and the opportunity to contribute to autonomous vehicle technology?“

2. Technical Phone Interview

Overview: Following the HR interview, I had a technical phone interview with a senior engineer. This round focused on my experience with data analysis, machine learning, and software engineering in the context of autonomous vehicles.

Key Areas Covered:

• Data Science and ML: Questions focused on how I’ve used machine learning models for evaluating autonomous systems, including data preprocessing, model selection, and validation.
• Software Engineering: I was asked about my experience with software design, debugging, and optimizing complex systems.
• SQL and Python: I was asked to write SQL queries and Python code to analyze large datasets and solve real-world problems.

Example Question: “How would you evaluate the performance of an autonomous vehicle’s perception system in an urban environment using data from simulation tests?“

3. Onsite Interview (Multiple Rounds)

The onsite consisted of several rounds, each focusing on different aspects of the role.

Round 1 - System Design

In this round, I was asked to design a system that could evaluate the performance of the AV’s software stack, ensuring it could handle large-scale data from sensors and simulations.

Example Question: “Design an end-to-end evaluation system that can collect, process, and report metrics on the safety and performance of an AV’s driving behavior across multiple environments.”

Round 2 - Coding and Problem Solving

This round tested my coding skills, particularly in Python and SQL. I was asked to solve problems related to data analysis, model evaluation, and performance optimization.

Example Question: “Here is a dataset of sensor data from autonomous vehicles. Write Python code to clean the data and identify any patterns or anomalies that may affect AV performance.”

Round 3 - Machine Learning and Data Analysis

This round focused on my ability to analyze AV performance data and derive actionable insights. I was given a dataset and asked to propose ways to improve the metrics used to evaluate the AV stack.

Example Question: “Given a large dataset from a fleet of autonomous vehicles, how would you design and implement a performance metric to track safety improvements over time?”

Round 4 - Behavioral and Leadership

As a staff engineer, leadership is key. This round focused on how I would manage cross-functional teams, resolve conflicts, and ensure collaboration across departments.

Example Question: “Tell us about a time when you led a cross-functional initiative involving both engineering and product teams. How did you manage priorities and ensure successful delivery?“

4. Final Round (Cultural Fit and Strategic Vision)

Overview: The final round involved discussions with senior leadership, focusing on alignment with Cruise’s values, strategic vision for the role, and long-term goals. This is where you’ll discuss how you can contribute to the company’s mission of advancing autonomous vehicles.
Example Question: “Where do you see the field of autonomy evaluation evolving in the next five years, and how would you contribute to that vision at Cruise?”

Key Skills and Experience

To succeed in this role, you need the following skills and experience:

• Machine Learning and Data Science: Experience in evaluating large models, training machine learning models, and analyzing big data to improve system performance.
• Software Engineering: Strong background in software development, particularly in Python, SQL, and other relevant languages for data processing and systems optimization.
• Statistical Analysis: A solid foundation in statistics, including hypothesis testing, statistical power, and identifying biases in data.
• Autonomous Vehicles Knowledge: Familiarity with autonomous vehicle systems and the challenges of developing and testing AV software.
• Leadership and Mentorship: Experience leading cross-functional teams, mentoring junior engineers, and driving technical initiatives across multiple departments.

What to Expect

• Complex System Design: Be prepared to design comprehensive evaluation systems for AVs, including defining metrics, running simulations, and optimizing for performance and safety.
• Machine Learning Challenges: You will likely face questions that test your ability to apply machine learning techniques to solve real-world problems in autonomous systems.
• Leadership and Collaboration: Expect to discuss how you have worked with diverse teams and managed large projects, with a strong focus on cross-functional collaboration.

Final Tips

• Prepare for System Design: Focus on how you would architect large systems for evaluating autonomous vehicles, ensuring scalability, reliability, and performance.
• Brush Up on Machine Learning: Be ready to explain how you would apply machine learning models to improve AV performance evaluation.
• Highlight Leadership Skills: As a staff engineer, you’ll need to show that you can lead teams, manage priorities, and mentor others.
• Know the AV Context: Understanding the challenges and technologies behind autonomous vehicles will help you stand out, even if you don’t have direct experience in the field.