Job Description
<div class="content-intro"><p><img style="display: none; max-width: 100%;" src="https://click.appcast.io/greenhouse-te8/a31.png?ent=34e=22630t=1701374353806" width="1px"> <img style="display: none; max-width: 100%;" src="https://track.jobadx.com/v1/i.gif?utm_pixel=224e990b-8ff4-4287-8d5d-2ff09647f181utm_ptz=ESTutm_rqt=track" alt="" width="1"></p></div><h1><strong>About the Team</strong></h1>
<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>
<h1><strong>About the Role</strong></h1>
<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 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 and the expectation is to work PST hours.</p>
<h1><strong>You’re excited about this opportunity because you will…</strong></h1>
<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 </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>
<h1><strong>We’re excited about you because…</strong></h1>
<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>6+ 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>
<h3><strong>Nice To Haves</strong></h3>
<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>
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<p style="font-weight: 500;">Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only</p>
<p>We use Covey as part of our hiring and/or promotional process for jobs in NYC and certain features may qualify it as an AEDT in NYC. As part of the hiring and/or promotion process, we provide Covey with job requirements and candidate submitted applications. We began using <a href="https://getcovey.com/product/covey-scout-inbound">Covey Scout for Inbound</a> from August 21, 2023, through December 21, 2023, and re ... (truncated, view full listing at source)