Interview Experience: Senior ML Systems Engineer II, ML Compute at Cruise

I recently interviewed for the Senior ML Systems Engineer II, ML Compute role at Cruise, and I’d like to share my experience to help others prepare. This role focuses on developing and optimizing the backend infrastructure that powers machine learning workflows, particularly those related to autonomous vehicles. The job requires deep knowledge of distributed systems, cloud technologies, and machine learning compute, with an emphasis on scalability, efficiency, and high performance.

Overview of the Role

The Senior ML Systems Engineer II, ML Compute at Cruise plays a critical role in designing, implementing, and maintaining the cloud infrastructure for machine learning (ML) workflows. This involves managing distributed compute systems, optimizing GPU/TPU usage, and ensuring that Cruise’s autonomous vehicle AI can scale and process data efficiently. As part of the team, you will work on cloud-agnostic solutions that support various ML needs across Cruise, optimizing for both cost and performance.

Interview Process

The interview process for this role is multi-phase and designed to test both your technical expertise and your ability to collaborate across teams.

1. Initial Screening (HR Interview)

Overview: The first step is a conversation with an HR recruiter who will review your resume, discuss your interest in the position, and confirm that your experience aligns with the requirements of the role. This is also when logistics like compensation and availability are discussed.

Example Question:
Why do you want to work at Cruise, and how does this position align with your career goals?

2. Technical Phone Interview

Overview: After the HR interview, the next step is a technical phone screen with a senior engineer or hiring manager. This round focuses on your technical knowledge, especially in distributed systems, cloud computing, and machine learning infrastructure.

Key Areas Covered:

• Distributed Systems: You will be asked about your experience designing scalable and fault-tolerant systems, especially for machine learning workloads.
• Cloud Platforms: Expect questions on your experience with cloud services like Google Cloud Platform (GCP), AWS, or Microsoft Azure, particularly for large-scale ML tasks.
• Programming: You may need to demonstrate proficiency in Python, Go, or C++ and solve coding problems related to system optimization.
• ML Systems: You may be asked about your experience working with GPU/TPU optimizations, ML model training pipelines, and orchestration tools.

Example Question:
How would you optimize a distributed ML pipeline to efficiently utilize GPUs for training deep learning models?

3. Onsite Interview (Multiple Rounds)

The onsite interview is a more comprehensive assessment and typically involves multiple rounds, each testing different aspects of your skill set.

Round 1 - Systems Design

You will be asked to design a system to support large-scale ML workflows. This could involve creating a cloud-based compute solution or improving an existing system for efficiency and scalability.

Example Question:
Design a scalable ML infrastructure that supports real-time data processing for autonomous vehicles. What tools and strategies would you use?

Round 2 - Optimization and Performance

In this round, the focus is on how you would optimize the performance of ML systems, particularly those involving distributed computing resources like GPUs or TPUs.

Example Question:
How would you optimize the utilization of GPUs in a multi-node environment to minimize costs while ensuring maximum throughput for model training?

Round 3 - Data Management and Workflow Orchestration

You will be asked to solve problems related to managing large-scale data, running ML workflows, and ensuring data consistency and integrity.

Example Question:
How would you build a system to orchestrate ML workflows, from data ingestion to model deployment, while ensuring reliability and scalability?

Round 4 - Behavioral and Leadership Assessment

This round assesses your ability to lead projects, mentor junior engineers, and collaborate with cross-functional teams. Expect questions about past projects, team dynamics, and leadership challenges.

Example Question:
Tell us about a time when you led a project to optimize a cloud-based infrastructure. What were the challenges, and how did you overcome them?

4. Final Round (Cultural Fit and Senior Leadership)

Overview: The final round typically involves discussions with senior leadership to assess your alignment with the company’s vision and culture. You may also discuss how you would contribute to the long-term success of the team and the company.

Example Question:
What do you see as the future of machine learning infrastructure, and how would you help Cruise lead the industry in this area?

Key Skills and Experience

To succeed in this role, you should have experience in the following areas:

• Distributed Systems: Strong background in designing and managing large-scale distributed systems, particularly those optimized for machine learning workloads.
• Cloud Platforms: Hands-on experience with cloud technologies (GCP, AWS, or Azure), particularly for high-performance compute tasks such as GPU/TPU usage.
• ML Systems and Orchestration: Experience with ML platforms, pipelines, and frameworks such as PyTorch, TensorFlow, and Ray.
• Programming: Proficiency in Python, C++, or Go, with a focus on optimizing systems and building scalable infrastructure.
• Performance Optimization: Experience with optimizing distributed compute resources, such as GPUs and TPUs, to maximize efficiency and minimize costs.
• Leadership: As a senior engineer, you will be expected to mentor junior engineers and drive projects from conception to implementation.

What to Expect

• Deep Technical Interviews: Prepare for in-depth technical questions related to system design, distributed computing, and machine learning infrastructure.
• Problem-Solving: Expect to solve problems that involve optimizing cloud infrastructure or improving the performance of ML pipelines.
• Leadership: Be prepared to discuss your experience leading projects, making strategic decisions, and collaborating with other teams.

Final Tips

• Brush Up on Distributed Systems: Review the principles of distributed systems, particularly for high-performance computing and large-scale data processing.
• Optimize for ML Workflows: Be ready to discuss how you would optimize the performance of machine learning workflows, especially in cloud environments.
• Understand Cruise’s Tech Stack: Familiarize yourself with the tools and platforms that Cruise uses, particularly around cloud services, GPUs/TPUs, and machine learning frameworks.
• Be Ready for Leadership Questions: As a senior engineer, leadership is key. Be prepared to discuss your experience leading technical projects and mentoring engineers.