Senior ML Engineer – AI Agents & LLMs (Hybrid)

DoorDash, Inc.; San Francisco (CA); Híbrido; USD 168.000 - 247.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

401(k) with employer matching

16 weeks paid parental leave

Medical, dental, and vision benefits

Paid time off and paid sick leave

11 paid holidays

Disability and basic life insurance

Descripción de la vacante

DoorDash, Inc. is seeking a Senior Machine Learning Engineer to join Agentic Foundations with a focus on Vertical Agent Development in a hybrid role based in San Francisco.

You will own continuous improvement of flagship agents, starting with the Ask Assistant, using an eval-driven loop to boost speed, reliability, and cost-efficiency for millions of customers. You will collaborate with teams on agent memory, representations, and post-training of small language models, shaping a roadmap for

Formación

• 3+ years of industry ML experience, including hands-on work building and shipping LLM-based agents and eval-driven improvements.
• Experience with post-training or fine-tuning open-weight models (e.g., SFT, preference optimization, or RL), ideally including small language models.
• 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.

Responsabilidades

• Own the continuous improvement of flagship agents, starting with the Ask Assistant, using an eval-driven loop.
• Balance quality, latency, and cost by running rigorous experiments and deploying small language models where appropriate.
• Launch new agentic experiences for Merchants and Dashers and turn foundations into production-ready agents.

Conocimientos

LLM-based agents

Post-training / fine-tuning

Python

NLP fundamentals

Educación

M.S./PhD in CS or Stats

Herramientas

PyTorch

TensorFlow

Descripción del empleo

DoorDash, Inc. is seeking a Senior Machine Learning Engineer to join Agentic Foundations with a focus on Vertical Agent Development in a hybrid role based in San Francisco.

You will own continuous improvement of flagship agents, starting with the Ask Assistant, using an eval-driven loop to boost speed, reliability, and cost-efficiency for millions of customers. You will collaborate with teams on agent memory, representations, and post-training of small language models, shaping a roadmap for