Senior Machine Learning Engineer - New Verticals Agentic Foundations

<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>About the Team</h2> <p>Agentic Foundations, part of New Verticals ML, builds the core AI/ML intelligence that powers high-quality, scalable agents at DoorDash, and proves it through flagship agent experiences such as the Ask grocery assistant. We operate as a closed loop: new models, evals, and representations make our agents better, and real-world agent usage surfaces the gaps that drive our next foundations work.&nbsp;</p> <h2>About the Role</h2> <p>We’re looking for a Senior Machine Learning Engineer to join Agentic Foundations with a primary focus on Vertical Agent Development. You will own the continuous improvement of our flagship agents, starting with the Ask Assistant, using an eval-driven loop to make them faster, more cost-efficient, and more reliable for millions of customers. You will also help us bring this approach to new agentic experiences for Merchants and Dashers, turning promising ideas into production-ready agents.</p> <p>This is a chance to work at the intersection of LLMs, agents, post-training, and evals on problems that ship. You will partner closely with engineers working on agentic memory, agent-compatible product representations, and post-training of small language models (SLMs), so that what we learn from production agents feeds directly back into our foundations, and what we build there makes our agents better.</p> <p>You will report into the engineering lead on our New Verticals AI/ML team. We expect this role to be hybrid with some time in-office and some time remote.</p> <h2>You’re excited about this opportunity because you will…</h2> <ul> <li><strong>Raise the quality of our Consumer agents:</strong> build and refine our eval harness-optimization loop to pinpoint failure modes, then iterate on prompts, tools, skills, and context, and measure the impact on real customer tasks.</li> <li><strong>Find the best balance of quality, latency, and cost:</strong> run rigorous experiments, including fine-tuning open-weights models and deploying small language models (SLMs) where they can replace or augment larger LLMs.</li> <li><strong>Launch new agents:</strong> take agentic opportunities for Merchants and Dashers from early exploration to production.</li> <li><strong>Turn foundations into product impact:</strong> work with teammates on agentic memory, agent-compatible product representations, and post-training SLMs for steerable generative recommendation, and bring those capabilities into production agents.</li> <li><strong>Shape the roadmap:</strong> partner with engineering, product, and business leaders to define an ML-driven strategy for our fast-growing grocery and retail delivery business.</li> </ul> <h2>We’re excited about you because you have…</h2> <ul> <li>3+ years of industry ML experience, including hands-on work building and shipping LLM-based agents (tool use, context management, prompting, guardrails) and improving them with evals and data</li> <li>Experience with post-training or fine-tuning of open-weights models (e.g., SFT, preference optimization, or RL), ideally including small language models, and sound judgment on quality, latency, and cost trade-offs</li> <li>Strong foundation in NLP and machine learning, with proficiency in Python and frameworks such as PyTorch or TensorFlow</li> <li>M.S. or PhD in Computer Science, Statistics, Math, or another quantitative field, or equivalent practical experience, plus a collaborative, growth-minded approach and a drive for measurable impact</li> </ul><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p><span style="font-size: 32px;"><stro