Job Description

About the Team

The Fraud Machine Learning team develops advanced models that are central to our anti-fraud systems. Operating at a large scale, we analyze billions of events across 20+ countries. We seek ML experts to help innovate and enhance the safety of DoorDash's logistics platform.

About the Role

We are looking for a passionate Applied Machine Learning Engineer to conceptualize, design, implement, and validate algorithms to prevent, detect, and mitigate fraud. The role involves improving our risk systems and requires expertise in production-level machine learning, solving end-user problems, and collaborating with multidisciplinary teams.

This position reports to the engineering manager of our Fraud Machine Learning team within the Operation Excellence organization. Post-pandemic, the role is expected to be hybrid, combining remote and in-office work.

Responsibilities

• Develop and deploy machine learning solutions to ensure a safe, seamless shopping experience.
• Collaborate with engineering and product teams to influence product roadmaps using ML.
• Mentor junior team members and lead cross-functional projects to achieve collective goals.

Qualifications

• 2+ years of industry experience in developing and deploying ML models (classical and deep learning).
• Proficiency with frameworks like PyTorch or TensorFlow.
• MS or PhD in a quantitative field such as Statistics, Computer Science, Math, etc.
• Familiarity with JVM languages (Kotlin/Scala).
• Knowledge of multi-task learning, LLMs, and anomaly detection.
• Domain expertise in fraud detection.

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