Interview Experience: Senior Applied Scientist II, Autonomy Evaluation at Cruise

I recently went through the interview process for the Senior Applied Scientist II, Autonomy Evaluation role at Cruise and am sharing a detailed account of my experience to help others prepare. This role is key to advancing autonomous vehicle technology by evaluating and enhancing the performance of the vehicle autonomy stack, using data science and machine learning techniques.

Overview of the Role

The Senior Applied Scientist II, Autonomy Evaluation at Cruise is focused on ensuring the safety, reliability, and overall performance of self-driving systems. The role involves evaluating large machine learning models, creating testing frameworks, designing metrics, and collaborating across teams to ensure that the self-driving system performs optimally under various real-world conditions.

Interview Process

The interview process at Cruise for this position is detailed and structured, involving multiple rounds focused on technical and leadership skills. Here’s a breakdown of the process:

1. Initial HR Screening

The process begins with an HR interview, where the recruiter will discuss your background, motivation for applying, and interest in Cruise’s mission. They will also go over basic qualifications such as your experience in machine learning, robotics, and autonomy evaluation.

Example Question:
What excites you about working at Cruise, particularly in the context of autonomous vehicles?

2. Technical Screening (Phone Interview)

This round typically involves a phone interview with a data scientist or a senior applied scientist. The focus here is on your technical expertise, particularly in the areas of machine learning, robotics, and autonomy evaluation.

Key Topics:

• Machine Learning: You might be asked to explain different types of machine learning models you have used, how you would approach training large-scale models, and the challenges faced during the training process.
• Evaluation Metrics: Expect to discuss how you would define and design metrics to evaluate autonomous vehicle performance in a variety of scenarios.
• Algorithms: Be prepared to answer questions on algorithms used for sensor fusion, object detection, or path planning in autonomous systems.
• Data Handling: You might be given a real-world dataset to analyze and asked how you would approach solving an evaluation problem.

Example Question:
How would you approach evaluating the performance of an autonomous vehicle’s perception system under varying weather conditions?

3. Onsite Interview (Multiple Rounds)

The onsite is typically split into several rounds, each focused on different aspects of your skillset.

Round 1 - Technical Problem Solving

You will likely be asked to solve complex problems related to autonomy evaluation. This may involve coding, explaining your approach to evaluating ML models, or designing a test framework for a specific autonomous system. You may also be asked to critique a model or suggest improvements.

Example Question:
How would you handle performance evaluation when multiple sensors are providing conflicting data in real-time?

Round 2 - Machine Learning Deep Dive

In this round, expect a deep dive into machine learning techniques. You’ll need to demonstrate your understanding of model architecture, hyperparameter tuning, and validation strategies. A solid understanding of reinforcement learning, deep learning, and time-series analysis is crucial.

Example Question:
Can you explain how you would design a reinforcement learning framework for improving an autonomous vehicle’s decision-making process?

Round 3 - System Design and Evaluation Framework

The focus here is on how you would design a complete autonomy evaluation framework that spans data collection, model testing, and performance validation. You may also be asked about how you ensure robustness and safety in autonomous systems.

Example Question:
If you were tasked with designing an evaluation framework for Cruise’s autonomous fleet, how would you go about validating its performance in urban environments?

Round 4 - Behavioral and Leadership Assessment

As a senior applied scientist, the interviewers will assess your leadership abilities, problem-solving skills, and how you handle complex, cross-functional projects.

Example Question:
Describe a time when you had to lead a team of engineers or data scientists to resolve a major challenge. How did you manage the project and ensure its success?

4. Final Round (Leadership and Strategic Fit)

The final round often involves discussions with senior leadership to assess your fit within the team and Cruise’s broader mission. You’ll likely be asked about your ability to lead strategic initiatives, mentor junior scientists, and drive cross-team collaboration.

Example Question:
How would you align your evaluation efforts with the broader company goals of safety and operational efficiency?

Key Skills and Experience

To succeed in this role, these are some of the key qualifications and skills you should have:

• Advanced ML/AI Knowledge: Experience with large-scale ML models, particularly those used in autonomous driving (e.g., object detection, sensor fusion, path planning).
• Robotics and Autonomy Evaluation: Hands-on experience evaluating autonomous vehicle systems and understanding the intricacies of real-time performance metrics.
• Software Engineering: Strong foundation in coding, especially in Python, C++, and frameworks like TensorFlow, PyTorch, or similar tools for machine learning.
• Statistical Analysis: Ability to design experiments, handle large datasets, and apply statistical methods to evaluate system performance (e.g., hypothesis testing, power analysis).
• Leadership: Proven experience leading teams, mentoring junior scientists, and managing multi-functional projects.
• Collaboration: Ability to work with cross-disciplinary teams, including hardware engineers, software developers, and product managers.

Final Tips

• Prepare for a Technical Deep Dive: Expect questions about evaluating autonomous vehicle performance, designing metrics, and solving technical problems on the fly.
• Review Core ML Concepts: Be sure to refresh your knowledge of machine learning algorithms, model evaluation, and large-scale data processing.
• Think Systematically: For design and evaluation questions, break down the problem systematically and demonstrate your approach to real-world challenges.
• Showcase Leadership: Be prepared to discuss past leadership experiences, particularly related to mentoring and leading technical teams.