Senior Machine Learning Engineer at DoorDash (San Francisco)

DoorDash connects customers with local businesses for delivery, from restaurants to grocery, retail and its own DashMart stores. 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.

Key responsibilities

• Build and refine our eval harness-optimization loop to pinpoint failure modes, then iterate on prompts, tools, skills, and context
• Find the best balance of quality, latency, and cost: run rigorous experiments, including fine-tuning open-weights models
• Take agentic opportunities for Merchants and Dashers from early exploration to production
• Work with teammates on agentic memory, agent-compatible product representations, and post-training SLMs for steerable generative recommendation
• Partner with engineering, product, and business leaders to define an ML-driven strategy for our fast-growing grocery and retail delivery business

Requirements

• 3+ years of industry ML experience, including hands-on work building and shipping LLM-based agents (tool use, context management, prompting
• Experience with post-training or fine-tuning of open-weights models (e.g., SFT, preference optimization, or RL)
• Strong foundation in NLP and machine learning, with proficiency in Python and frameworks such as PyTorch or TensorFlow
• Or PhD in Computer Science, Statistics, Math, or another quantitative field, or equivalent practical experience, plus a collaborative

Location and schedule

• Onsite in San Francisco, California
• 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

Salary and benefits

US base salary $167,800 to $246,800 a year. DoorDash also offers benefits; the full list is on the posting.

How to apply

Apply on the company's own site.