Engineering Manager, Machine Learning Platform

As an Engineering Manager for the Machine Learning Platform at DoorDash, your primary responsibilities will include overseeing the redesign and operations of the Feature Store, leading a high-performing engineering team, and collaborating closely with ML managers and product teams. You'll ensure data consistency and low-latency access for real-time features while solving complex engineering challenges to enhance model performance.

To qualify for the Engineering Manager position at DoorDash, candidates should have over 8 years of industry experience in software engineering, machine learning, or infrastructure, alongside at least 2 years in an engineering management role. A background in building and managing large-scale ML platforms, experience with feature stores, and proficiency in cloud-based environments like AWS, GCP, or Azure are essential.

The Engineering Manager role at DoorDash is impactful because it positions you at the center of our machine learning strategy. You will directly influence the success of various ML models, work with a quality-driven team to accelerate development, and ensure the integrity and reliability of our systems, shaping business outcomes that elevate the customer experience across our platform.

DoorDash prides itself on a vibrant company culture that emphasizes leadership, learning, and collaboration. Our team members are empowered to take initiative and express their unique perspectives. We promote technical excellence and innovation, making it a dynamic environment for professionals to grow and thrive as they contribute to solving real-world challenges.

In the Engineering Manager role at DoorDash, you will actively mentor your team, promoting a strong culture of professional development. The company supports ongoing learning opportunities and helps engineers explore their interests while contributing effectively to large-scale machine learning projects, allowing for both personal and career growth.

To answer this question effectively, describe your hands-on experience with machine learning platforms, specifically your role in building, maintaining, or improving their components. Discuss your approach to feature engineering and ensuring data accessibility, as well as any challenges you addressed during your tenure.

Share specific examples of how you've encouraged collaboration in past roles. Discuss tools or frameworks you've implemented to streamline communication and ensure that engineering efforts align with product objectives, emphasizing the importance of cross-functional teamwork.

Describe strategies such as setting clear expectations, providing consistent feedback, and encouraging autonomy while also promoting a culture of technical excellence. Share how mentorship and skill development play a crucial role in your management style.

Discuss your systematic approach to problem-solving, including breaking down challenges into manageable components, consulting with your team, and leveraging data-driven decisions to guide your solutions. Provide examples from your past experiences.

Reflect on your understanding of feature stores and how you've utilized them in past roles. Discuss the importance of data consistency, low latency, and accessibility in ML projects, demonstrating both technical knowledge and practical application.

Illustrate your commitment to continuous learning. Mention relevant courses, conferences, or professional communities you engage with. Explain how staying informed impacts your effectiveness as an Engineering Manager and contributes to your team's success.

Prepare to share a detailed account of a machine learning project, outlining your role, the objectives, the technologies used, and the impact of the project on business outcomes. Highlight any unique challenges and how you overcame them.

Discuss the criteria you consider important in evaluating candidates. Share your approach to assessing both technical capabilities and cultural fit, elaborating on how these factors contribute to building a high-performing team.

Explain your understanding of high availability principles in designing machine learning systems. Discuss specific strategies you've implemented to minimize downtime and ensure systems are resilient and reliable.

Elaborate on your approach to performance feedback, emphasizing the importance of conducting regular check-ins, offering constructive criticism, and celebrating successes. Stress your commitment to creating an environment where team members feel supported.