Software Engineer, Machine Learning - Fraud

DoorDash; San Francisco (CA); Hybrid; USD 100,000 - 150,000; Full time

Job summary

An innovative firm is seeking a passionate Applied Machine Learning Engineer to join their Fraud Machine Learning team. This role involves conceptualizing and implementing algorithms to enhance fraud detection systems, ensuring a safe shopping experience for users. You will work closely with engineering and product teams, leveraging your expertise in machine learning to influence product development. The position offers a hybrid work environment, allowing for flexibility while contributing to impactful projects. If you're eager to innovate and drive safety in logistics, this opportunity is perfect for you.

Qualifications

• 2+ years of experience in developing ML models.
• Proficiency with ML frameworks like PyTorch or TensorFlow.

Responsibilities

• Develop and deploy ML solutions for fraud detection.
• Collaborate with teams to influence product roadmaps.

Skills

Machine Learning

Deep Learning

Fraud Detection

Statistical Analysis

Collaboration

Education

Master's in Statistics

PhD in Computer Science

Tools

PyTorch

TensorFlow

Kotlin

Scala

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.