Software Engineer, Machine Learning Infrastructure - job 1 of 2

As a Software Engineer specializing in Machine Learning Infrastructure at DoorDash, you will be responsible for building world-class ML platforms that enable seamless model development, training, and deployment. Collaborating with data scientists and product engineers to evolve the ML platform is key, along with designing high-performing data pipelines that support the rapid implementation of new technologies. You will also play a pivotal role in ensuring scalability and reliability of training and inference infrastructure, ultimately driving the machine learning capabilities across DoorDash's global operations.

To qualify for the Software Engineer, Machine Learning Infrastructure role at DoorDash, candidates should possess a B.S., M.S., or PhD in Computer Science or a related field. You should also have at least 6 years of industry experience in software engineering, with a proven track record of building machine learning systems for production. Familiarity with cloud computing environments and strong knowledge of fundamental computer science concepts and object-oriented programming languages is critical for success in this position.

Essential technical skills for the Software Engineer, Machine Learning Infrastructure role at DoorDash include proficiency in programming languages such as Python, especially with machine learning libraries like Pandas, PyTorch, and TensorFlow. Experience in distributed systems, data processing pipelines, and cloud-based environments, preferably AWS, is also important. Candidates should be familiar with tools such as Spark, MLLib, and MLFlow, as well as concepts involving large language models and real-time computing challenges.

The work environment for Software Engineers, Machine Learning Infrastructure at DoorDash is collaborative and fast-paced, thriving on innovation and inclusivity. As this role is hybrid, you will have the flexibility to work from home and participate in in-office activities in Seattle, which fosters a strong team dynamic. DoorDash emphasizes supporting its employees through comprehensive benefits, promoting happiness, health, and well-being, creating an engaging setting to tackle complex challenges.

DoorDash is committed to employee growth and development within the Software Engineer, Machine Learning Infrastructure role by providing a culture that encourages learning and innovation. You will have opportunities to work on groundbreaking projects that have a considerable impact on the company, gain insights from diverse perspectives, and use the latest technologies. Through comprehensive benefits and a focus on well-being, DoorDash creates an environment where team members can thrive and advance their careers.

In answering this question, highlight specific projects where you've built and deployed machine learning models. Discuss the tools and technologies you used, such as TensorFlow or PyTorch, and the outcomes of your work. It's also beneficial to mention any challenges you faced and how you overcame them to show your problem-solving skills.

To address this, talk about your experience designing infrastructures that handle large-scale data and predictions. Discuss methods you've implemented to enhance reliability, such as monitoring systems for performance metrics or using cloud environments that can easily scale resources. Share examples of systems you've built that required attention to scalability from day one.

Describe specific challenges you’ve encountered in previous projects related to data processing, such as handling incomplete data or ensuring pipeline efficiency. Explain the approaches you took to resolve these issues, showcasing your analytical skills and creativity in finding solutions.

Detail a project where your collaboration with data scientists led to significant outcomes. Highlight how you integrated their understanding of data with your engineering skills to create solutions that benefited the overall project. Discuss the communication strategies you employed that helped bridge the gap between engineering and data science.

Here, you should mention the programming languages you excel in, particularly those relevant to the role, like Python. Discuss any specific libraries or tools you’ve used, such as TensorFlow or Spark, and provide examples of how you’ve applied them in your work. This will demonstrate your technical strengths to the interviewer.

Discuss your strategies for staying informed about the latest developments in machine learning and artificial intelligence. Mention any resources like online courses, conferences, or research papers that you regularly follow, and give examples of how you’ve applied new trends or techniques in your work.

Talk about your process for debugging and optimizing models, including specific tools or techniques like cross-validation, hyperparameter tuning, or monitoring performance metrics. Give tangible examples of how your strategies led to improved outcomes in projects.

Describe your methodology for testing models before deployment, touching on strategies like A/B testing, validation sets, and performance tracking. Highlight your understanding of testing as an essential component of model development, ensuring that models perform adequately in real-world scenarios.

Explain the importance you place on documentation for machine learning projects. Discuss how you document requirements, workflows, and outcomes to ensure clarity for yourself and other team members. Emphasize that good documentation enhances collaboration, preserves knowledge, and streamlines future iterations or changes.

In response, describe a specific instance where you encountered a setback during a project. Discuss how you identified the issue, the steps you took to address it, and the lessons learned from the experience. This will showcase your resilience and problem-solving skills, which are vital for any engineering position.