Senior Machine Learning Engineer - New Verticals Agentic Foundations

Engg; San Francisco, Sunnyvale, Seattle, New York (CA, CA, WA, NY); Hybrid; USD 204,000 - 299,000; Full time

Benefits offered by this job

Premium healthcare

401(k) with employer matching

Paid parental leave

Wellness benefits

Commuter benefits match

Paid time off

Job summary

DoorDash seeks a Senior Machine Learning Engineer to join Agentic Foundations, focusing on vertical agent development and production-ready agents like the Ask Assistant. You will own improvements, optimize for quality, latency, and cost, and partner across teams to scale ML-driven grocery and retail delivery initiatives.

The role involves working at the intersection of LLMs, agents, post-training, and evals, with hybrid in-office and remote work.

Qualifications

• 3+ years of industry ML experience with hands-on work shipping LLM-based agents (tool use, context management, prompts, guardrails) and evals/data improvements.
• Experience with post-training or fine-tuning open-weights models (SFT, preference optimization, or RL), including small language models.
• Strong foundation in NLP and ML with proficiency in Python and frameworks such as PyTorch or TensorFlow

Responsibilities

• Raise the quality of our Consumer agents: 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.
• Find the best balance of quality, latency, and cost: run rigorous experiments, including fine-tuning open-weights models and deploying small language models (SLMs) where they can 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 with teammates on agentic memory, agent-compatible product representations, and post-training SLMs for steerable generative recommendation, and bring those capabilities into production agents.
• Shape the roadmap: partner with engineering, product, and business leaders to define an ML-driven strategy for our fast-growing grocery and retail delivery business.

Skills

LLM-based agents

Post-training / fine-tuning

NLP foundations

Python

PyTorch

TensorFlow

Education

MS/PhD in CS/Statistics/Math

Tools

PyTorch

TensorFlow

Job description

About the Team

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.

About the Role

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-efficiency, 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. 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. 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.

Responsibilities

• Raise the quality of our Consumer agents: 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.
• Find the best balance of quality, latency, and cost: run rigorous experiments, including fine-tuning open-weights models and deploying small language models (SLMs) where they can 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 with teammates on agentic memory, agent-compatible product representations, and post-training SLMs for steerable generative recommendation, and bring those capabilities into production agents.
• Shape the roadmap: partner with engineering, product, and business leaders to define an ML-driven strategy for our fast-growing grocery and retail delivery business.

Qualifications

• 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 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
• Strong foundation in NLP and machine learning, with proficiency in Python and frameworks such as PyTorch or TensorFlow
• 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

Compensation

Base salary is localized according to an employee’s work location. In addition to base salary, the compensation for this role includes opportunities for equity grants. The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Ranges are market-dependent and may be modified in the future.

• I4 $137,100 - $201,600 USD
• I5 $167,800 - $246,800 USD
• I6 $203,500 - $299,300 USD

Benefits

DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act).

DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.

Paid Time Off Details

For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.

For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).

About DoorDash

At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started by enabling door-to-door delivery, and we are looking for team members who can help us go from a company that is known as the place you order food to a company that people turn to for any and all goods. DoorDash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, and own their careers. We're committed to supporting employees’ happiness, healthiness, and overall well-being by providing comprehensive benefits and perks including premium healthcare, wellness expense reimbursement, paid parental leave and more. Our Comm