Senior Machine Learning Engineer, New Verticals Agentic Foundations

DoorDash USA is hiring a Senior Machine Learning Engineer for Agentic Foundations, with a focus on Vertical Agent Development. This role drives ongoing improvements to flagship agents and extends an eval-driven approach to new agentic experiences for merchants and dashers.

Location and Work Model

Seattle, WA (hybrid)

This position includes some time in-office and some time remote.

Compensation

USD 167,800 - 246,800 per year

Role Summary

The team will own continuous improvement of core agent experiences, beginning with the Ask Assistant. Responsibilities include maintaining an eval-driven loop, reducing latency and cost while improving reliability, and scaling the approach into new merchant and dasher agentic workflows.

Key Responsibilities

• Own continuous improvement of flagship agents, starting with the Ask Assistant, using an eval-driven loop to improve speed, cost-efficiency, and reliability for millions of customers.
• Apply the same approach to new agentic experiences for Merchants and Dashers, converting promising concepts into production-ready agents.
• Build and refine an eval harness and optimization loop to identify failure modes, then iterate on prompts, tools, skills, and context while measuring impact on real customer tasks.
• Conduct rigorous experiments, including fine-tuning open-weights models and deploying small language models (SLMs) to replace or augment larger LLMs.
• Progress agentic opportunities from early exploration through production for Merchants and Dashers.
• Collaborate on agentic memory, agent-compatible product representations, and post-training SLMs for steerable generative recommendation, bringing these capabilities into production agents.
• Partner with engineering, product, and business leaders to define an ML-driven strategy for DoorDash’s fast-growing grocery and retail delivery business.

Required Qualifications

• 3+ years of industry machine learning experience, including hands-on building and shipping of LLM-based agents (tool use, context management, prompting, guardrails) and improving them with evals and data.
• Experience with post-training and/or fine-tuning of open-weights models (such as SFT, preference optimization, or RL), ideally involving small language models, with sound judgment across quality, latency, and cost trade-offs.
• Strong foundation in NLP and machine learning with proficiency in Python and experience using PyTorch or TensorFlow.
• M.S. or PhD in Computer Science, Statistics, Math, or another quantitative field, or equivalent practical experience, along with a collaborative, growth-minded approach and drive for measurable impact.

Technologies

• Python
• PyTorch
• TensorFlow
• SFT
• Preference optimization
• RL

Team Overview

Agentic Foundations is part of New Verticals ML. The team builds the core AI/ML intelligence that powers high-quality, scalable agents at DoorDash and validates it through flagship agent experiences such as the Ask grocery assistant. The group operates in a closed loop where new models, evals, and representations improve agents, and real usage helps surface gaps that inform the next foundations work.

Benefits

• Premium healthcare
• Wellness expense reimbursement
• Paid parental leave

Additional Information

You will report into the engineering lead on DoorDash’s New Verticals AI/ML team.

Notice to applicants for jobs located in NYC or remote jobs associated with an office in NYC only:

• DoorDash used Covey as part of the hiring and/or promotional process for jobs in NYC, and certain features may qualify it as an AEDT in NYC.
• Covey received job requirements and candidate submitted applications.
• Covey Scout for Inbound was used from August 21, 2023 through December 21, 2023.
• Covey Scout for Inbound was resumed on June 29, 2024 and ceased on April 30, 2026.
• The Covey tool has been reviewed by an independent auditor. Results may be viewed at: https://getcovey.com/nyc-local-law-144.
• Beware of recruitment scams: DoorDash, Deliveroo, and Wolt will never ask you to pay money or share sensitive financial information during hiring.