Staff Machine Learning Software Engineer, Perception Interview Experience at Cruise

I recently interviewed for the Staff Machine Learning Software Engineer, Perception position at Cruise and would like to share my experience to help others who are preparing for similar roles. This position is crucial for the development of perception systems in autonomous vehicles (AVs), and it requires both deep expertise in machine learning (ML) and practical software engineering skills to design and implement perception evaluation systems at scale.

Overview of the Role

As a Staff Machine Learning Software Engineer in Perception at Cruise, your primary responsibility is to build and improve machine learning models that help autonomous vehicles understand their environment. This includes developing scalable ML evaluation frameworks, ensuring the quality and performance of models, and collaborating with cross-functional teams to drive advancements in AV perception systems. You will also be responsible for the technical direction of perception evaluation, including designing and implementing new solutions to test and validate perception algorithms.

Interview Process

The interview process for this role was extensive and challenging. It was divided into several stages, each evaluating different aspects of my qualifications and ability to work in a fast-paced, high-stakes environment.

1. Initial Screening (HR Interview)

Overview: The process began with a screening call from an HR recruiter. This conversation focused on my background, motivation for applying, and understanding of Cruise’s mission. The recruiter provided a detailed overview of the job requirements and expectations.

Example Question:

• “What excites you about working with Cruise, and what specific experiences make you a good fit for the Staff ML Software Engineer role?“

2. Technical Phone Interview

Overview: After passing the HR screen, I had a technical interview with an engineer from the Perception team. This interview was a deep dive into my technical expertise, especially around ML systems, computer vision, and perception algorithms.

Key Areas Covered:

• Machine Learning Systems: We discussed my experience working with various ML frameworks like TensorFlow, PyTorch, and others, and how I’ve implemented or optimized models for real-world applications.
• Perception Algorithms: I was asked questions about perception tasks in autonomous vehicles, such as object detection, classification, sensor fusion, and how to measure model performance.
• Software Engineering: I was asked about my experience with software development practices, including coding standards, testing, and version control.

Example Question:

• “How would you evaluate the performance of a perception model that detects objects from sensor data? What metrics would you use and why?“

3. Onsite Interview (Multiple Rounds)

The onsite consisted of several rounds, each focusing on different skills:

Round 1 - System Design

In this round, I was asked to design a scalable ML evaluation system for testing perception algorithms. The interviewer wanted to know how I would architect the system, select appropriate tools, and ensure scalability as Cruise expands its fleet.

Example Question:

• “Design a distributed system to evaluate perception models for real-time decision-making in autonomous vehicles. What technologies would you use, and how would you ensure high availability and low latency?”

Round 2 - Coding Challenge

This round focused on writing efficient code. I was given a problem related to data processing and asked to implement a solution in Python or C++. The problem tested my ability to write clean, optimized code to handle large datasets.

Example Question:

• “Given a dataset of sensor readings from an autonomous vehicle, write a function in Python to process and clean the data, removing noise and anomalies.”

Round 3 - Deep Dive into ML

In this round, the focus was on my experience with the ML lifecycle—training models, validating them, and deploying them in production. I was also asked about how I deal with the challenges of ML in real-world applications like autonomous driving.

Example Question:

• “How would you handle model drift in perception systems? What techniques would you use to retrain the model and ensure it adapts to new data?”

Round 4 - Behavioral and Leadership Assessment

As a senior-level engineer, I was asked to demonstrate leadership qualities, problem-solving abilities, and my approach to working with cross-functional teams.

Example Question:

• “Tell us about a time when you led a project that involved collaboration with cross-functional teams. How did you ensure alignment, and what was the outcome?“

4. Final Round (Cultural Fit and Vision Alignment)

Overview: The final round involved meeting with senior leadership to discuss my vision for the role, how I aligned with Cruise’s mission, and how I would approach scaling perception systems in the context of the company’s goals.

Example Question:

• “Where do you see the field of perception systems in autonomous vehicles evolving over the next few years, and how would you contribute to shaping that future at Cruise?”

Key Skills and Experience

To excel in this role, the following skills and experience are essential:

• Machine Learning and Computer Vision: Proficiency in ML frameworks like TensorFlow and PyTorch, with deep experience in perception tasks such as object detection, segmentation, and sensor fusion.
• Software Engineering: Strong coding skills in Python and C++, with a focus on writing efficient, maintainable code.
• Data Processing: Expertise in designing and implementing data pipelines to process large volumes of data from sensors like LiDAR, cameras, and radar.
• System Design: Experience in designing scalable systems for ML model evaluation and performance tracking.
• Leadership: Experience leading technical projects, collaborating with cross-functional teams, and driving innovation in perception solutions.

What to Expect

• System Design: Be ready to discuss designing and scaling systems for evaluating perception models, handling large datasets, and ensuring real-time performance.
• Deep Technical Interviews: Expect a focus on your technical knowledge of ML, perception, and computer vision systems, particularly in autonomous vehicles.
• Behavioral Questions: Be prepared to demonstrate leadership and your ability to manage projects, resolve conflicts, and collaborate with various teams.

Final Tips

• Prepare for System Design: Make sure you’re comfortable with designing distributed systems and architectures that can handle large-scale ML evaluations for AVs.
• Brush Up on ML and CV: Make sure you are up to date with the latest advancements in ML and computer vision, especially related to autonomous vehicles.
• Leadership and Collaboration: Be prepared to talk about your leadership style, cross-team collaboration, and how you manage complex projects.