ML Engineer: Delivery & Logistics Optimization

DoorDash ANZ; San Francisco (CA); On-site; USD 137,000 - 299,000; Full time

Benefits offered by this job

Equity grants

401(k) with employer matching

Paid parental leave

Job summary

DoorDash is seeking a Machine Learning Engineer on the Drive team in San Francisco area to own end-to-end ML systems—from feature engineering to deployment and monitoring. You will build next‑generation models for ETA estimation, order release, and logistics optimization using PyTorch and large-scale signals.

You will work with engineers, product managers, and data scientists to apply RL, optimization, and multimodal AI, driving production quality and scalable solutions across a high‑impact

Qualifications

• 5+ years of industry experience building and shipping production ML systems with measurable business impact.
• Strong experience with PyTorch and distributed processing tools such as Spark and Airflow.
• Experience building, deploying, monitoring, and maintaining production ML systems end-to-end.
• Strong Python software engineering skills and ML infrastructure tooling.
• Deep expertise in at least one area: Deep Learning, RL, OR, LLMs/VLMs.

Responsibilities

• Own ML systems end-to-end from feature engineering to deployment, monitoring, and iteration.
• Build models for ETA predictions, optimization, and decision-making in logistics.
• Develop deep learning models using large-scale signals and multimodal data.
• Apply RL and optimization to improve logistics decisions and marketplace efficiency.
• Collaborate with engineers, product, data scientists, and platform teams to productionize capabilities.

Skills

Python

PyTorch

Deep Learning

Reinforcement Learning

Optimization/Operations Research

LLMs / VLMs

Production ML Systems

Distributed Data Processing

Education

Bachelor’s/Master’s/PhD in CS/ML

Tools

Claude Code

Codex

Cursor

Job description

DoorDash is seeking a Machine Learning Engineer on the Drive team in San Francisco area to own end-to-end ML systems—from feature engineering to deployment and monitoring. You will build next‑generation models for ETA estimation, order release, and logistics optimization using PyTorch and large-scale signals.

You will work with engineers, product managers, and data scientists to apply RL, optimization, and multimodal AI, driving production quality and scalable solutions across a high‑impact