Software Engineer, Machine Learning - Fraud

DoorDash USA•Sunnyvale, CA

13h•Hybrid

About The Position

The Fraud Machine Learning team builds cutting edge models that play central roles in our important anti-fraud systems. We operate at a massive scale, risking billions of events across 20+ countries. We are looking for ML experts who can help us push the technology boundaries and make DoorDash the world’s most safe logistics engine! We’re looking for a passionate Applied Machine Learning engineer to join our team. As a Machine Learning Engineer, you’ll be conceptualizing, designing, implementing, and validating algorithmic solutions to prevent, detect and mitigate fraud. You are excited about opportunities to introduce step function changes to our risking system. You will demonstrate a command of production level machine learning, experience solving end-user problems, and collaborate well with multi-disciplinary teams. You will report into the engineering manager on our Fraud Machine Learning team in our Operation Excellence organization. Once our offices reopen, we expect this role to be hybrid with some time in-office and some time remote.

Requirements

2+ years of industry experience developing machine learning models ( both classical and deep learning) and shipping ML solutions to production.
Experience with PyTorch or TensorFlow or similar deep learning framework.
M.S., or PhD. in Statistics, Computer Science, Math, Operations Research, Physics, Bioinformatics, Economics, or other quantitative fields
Familiarity with a JVM language (Kotlin/Scala)
Familiarity with multi-task learning, LLMs and anomaly detection
Fraud domain knowledge

Responsibilities

Develop production machine learning solutions to build a safe and frictionless shopping experience for a diverse and expanding retail space.
Partner with engineering and product leaders to help shape the product roadmap applying ML
Mentor junior team members, and lead cross functional pods to create collective impact