Software Engineer, Machine Learning Platform - MDX

<div class="content-intro"><p><img style="display: none; max-width: 100%;" src="https://click.appcast.io/greenhouse-te8/a31.png?ent=34&amp;e=22630&amp;t=1701374353806" width="1px"> <img style="display: none; max-width: 100%;" src="https://track.jobadx.com/v1/i.gif?utm_pixel=224e990b-8ff4-4287-8d5d-2ff09647f181&amp;utm_ptz=EST&amp;utm_rqt=track" alt="" width="1"></p></div><h2><strong>About the Team</strong></h2> <p>Come help us build the world's most reliable on-demand, logistics engine for delivery! We're bringing on talented engineers to help us create and maintain a 24x7, no downtime, global infrastructure system that powers DoorDash’s three-sided marketplace of consumers, merchants, and dashers.</p> <h2><strong>About the Role</strong></h2> <p>At DoorDash, our Data Scientists and ML Engineers have the opportunity to dive into a wealth of delivery data to improve company-wide ML workflows such as Search &amp; Recommendations, Dasher Assignment, ETA Prediction, and Dasher Capacity Planning. You will join a small team to build systems that empower efficient machine learning at scale. This is a hybrid opportunity in San Francisco, Sunnyvale or Seattle.</p> <h2>You’re excited about this opportunity because you will…</h2> <ul> <li>Build a world-class ML platform where models are developed, trained, and deployed seamlessly</li> <li>Work closely with Data Scientists and Product Engineers to evolve the ML platform as per their use cases&nbsp;</li> <li>You will help build high performance and flexible pipelines that can rapidly evolve to handle new technologies, techniques and modeling approaches</li> <li>You will work on infrastructure designs and solutions to store trillions of feature values and power hundreds of billions of predictions a day</li> <li>You will help design and drive directions for the centralized machine learning platform that powers all of DoorDash's business.</li> <li>Improve the reliability, scalability, and observability of our training and inference infrastructure.</li> </ul> <h2><strong>We’re excited about you because…</strong></h2> <ul> <li>B.S., M.S., or PhD. in Computer Science or equivalent</li> <li>Exceptionally strong knowledge of CS fundamental concepts and OOP languages</li> <li>2+ years of industry experience in software engineering</li> <li>Prior experience building machine learning systems in production such as enabling data analytics at scale</li> <li>Prior experience in machine learning - you've developed and deployed your own models - even if these are simple proof of concepts</li> <li>Systems Engineering - you've built meaningful pieces of infrastructure in a cloud computing environment. Bonus if those were data processing systems or distributed systems</li> </ul> <h2><strong>Nice To Haves</strong></h2> <ul> <li>Experience with challenges in real-time computing</li> <li>Experience with large scale distributed systems, data processing pipelines and machine learning training and serving infrastructure</li> <li>Familiar with Pandas and Python machine learning libraries and deep learning frameworks such as PyTorch and TensorFlow</li> <li>Familiar with Spark, MLLib, Databricks,MLFlow, Apache Airflow, Dagster and similar related technologies.</li> <li>Familiar with large language models like GPT, LLAMA, BERT, or Transformer-based architectures</li> <li>Familiar with a cloud based environment such as AWS</li> </ul><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p><span style="font-size: 32px;"><strong>Compensation</strong></span></p> <p>The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.</p> <p>In addition to base salary, the compensation for this role inc