Senior Machine Learning Engineer, New Verticals Agentic Foundations
DoorDash USA · San Francisco, CA; Sunnyvale, CA; Seattle, WA; NewYork, NY
About this role
## About the Team Agentic Foundations (part of New Verticals ML) builds the core AI/ML intelligence that powers high-quality, scalable agents at DoorDash—proving it through flagship agent experiences like the Ask grocery assistant. We operate as a closed loop: new models, evals, and representations improve agents, and real-world agent usage reveals gaps that drive our next foundations work. ## About the Role DoorDash is hiring a **Senior Machine Learning Engineer** to join **Agentic Foundations**, with a primary focus on **Vertical Agent Development**. You’ll 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’ll also help bring this approach to new agentic experiences for **Merchants and Dashers**, turning promising ideas into **production-ready agents**. This role sits at the intersection of **LLMs, agents, post-training, and evals**—on problems that ship. You’ll partner with teams working on **agentic memory**, **agent-compatible product representations**, and **post-training of small language models (SLMs)** so production learnings feed back into foundations, and foundations improvements strengthen production agents. **Reporting:** Engineering lead on DoorDash’s New Verticals AI/ML team. **Work model:** Hybrid (some time in-office, some time remote). ## You’ll Do - **Raise the quality of Consumer agents:** Build and refine an eval harness/optimization loop to pinpoint failure modes, then iterate on prompts, tools, skills, and context—measuring impact on real customer tasks. - **Find the best balance of quality, latency, and cost:** Run rigorous experiments, including fine-tuning open-weights models and deploying SLMs to replace or augment larger LLMs. - **Launch new agents:** Take agentic opportunities for Merchants and Dashers from early exploration to production. - **Turn foundations into product impact:** Work on agentic memory, agent-compatible product representations, and post-training SLMs for steerable generative recommendation—then bring those capabilities into production agents. - **Shape the roadmap:** Partner with engineering, product, and business leaders to define an ML-driven strategy for DoorDash’s fast-growing grocery and retail delivery business. ## You’re Excited Because You Have - **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**. - Experience with **post-training or fine-tuning open-weights models** (e.g., SFT, preference optimization, or RL), ideally including **small language models**, plus strong judgment on **quality/latency/cost** trade-offs. - Strong foundation in **NLP and machine learning**, with proficiency in **Python** and frameworks such as **PyTorch** or **TensorFlow**. - **M.S. or PhD** in CS, Statistics, Math, or another quantitative field (or equivalent practical experience), with a collaborative, growth-minded approach and drive for measurable impact. ## Compensation (US) - **National base pay range:** **$167,800 – $246,800 USD** - Equity grants may be included. ## About DoorDash DoorDash’s mission empowers local economies. The company is looking for team members who can help it grow from a food-ordering platform into a destination for all goods. DoorDash also emphasizes employ
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