Staff Software Engineer, ML Serving Platform

As a Staff Software Engineer focusing on the ML Serving Platform at DoorDash, your main responsibilities include leading the technical direction for a high-throughput serving platform, ensuring zero-downtime operations, and developing innovative solutions to improve model deployment workflows. You'll be the go-to person for architectural decisions and will collaborate closely with ML Engineers and Infrastructure teams to optimize the serving stack.

To be successful as a Staff Software Engineer at DoorDash, you should have over 8 years of industry experience in software engineering along with at least a year in a technical lead role. A solid foundation in distributed systems and programming languages like Python, Go, Kotlin, C++, or Java is essential. Experience with production ML systems and a genuine passion for reliability and performance will set you apart in this role.

Preparing for a Staff Software Engineer position at DoorDash involves focusing on your technical skills in distributed systems, programming expertise, and practical knowledge of machine learning frameworks. Brush up on your experience with high-QPS services, and get familiar with cloud computing platforms as well as ML lifecycle management to ensure you understand the challenges and solutions related to ML serving.

The culture at DoorDash for the Staff Software Engineer role is collaborative and innovative. The company values diverse perspectives, and you'll work alongside experienced professionals who are passionate about technology and helping each other grow. Mentorship and sharing best practices are encouraged, contributing to a positive and inclusive team environment.

Yes, the Staff Software Engineer position at DoorDash is a hybrid opportunity, allowing you to work from either San Francisco, Sunnyvale, Seattle, or New York. This flexibility is designed to help you balance remote work with valuable in-person collaboration.

When answering this question, focus on the architecture you used, the technologies involved, and the challenges faced during the development process. Discuss how you ensured high availability and scalability while managing latency, and what strategies you used for monitoring and performance optimization.

It's crucial to explain your approach to CI/CD, blue-green deployments, or canary releases. Highlight any tools you've used for managing deployments and discuss strategies for health checks and rollbacks to ensure reliability during updates.

Emphasize the importance of scalability, performance, and observability. Talk about the techniques for managing high-QPS workloads, how to handle feature lookups, and the need for real-time inference. Your answer should reflect your understanding of both the technical and operational aspects of running ML systems.

Discuss your communication strategy, tools you utilize for collaborative work, and how you ensure alignment on goals and milestones across teams. Provide examples of successful cross-team projects and any lessons learned.

Be honest about your proficiency in programming languages relevant to this role, such as Python, Go, or Java. Discuss any frameworks you have experience with, particularly those related to machine learning, and how you have employed them in projects.

Detail your involvement in implementing feature stores, focusing on how you ensured data consistency, availability, and latency requirements were met. Discuss any challenges you've overcome and the overall impact on machine learning models.

Mention specific strategies you use to mentor junior engineers, such as conducting code reviews, leading hands-on training sessions, or providing constructive feedback. Share examples of how your mentorship has positively influenced their growth.

Explain your approaches to optimizing workflows, like introducing automation in deployment processes, documentation standards, or creating reusable components. Discuss outcomes related to reduced time-to-production and team productivity.

Share a concrete example of a conflict, explaining the situation, the stakeholders involved, and the communication strategies you used to find common ground. Highlight the importance of empathy and mutual understanding.

This question is subjective, but focus on aspects like the company’s mission, the innovative environment, or the opportunities for personal and professional growth. Align your excitement with the core values and vision of DoorDash.