Senior Software Engineer - ML/ Fraud Prevention

About Waymo

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.

You will:

• Train, evaluate, and ship production machine learning models to score rider risk in real time, directly protecting revenue from payment fraud and reducing vehicle vandalism.
• Engineer and maintain high-throughput, low-latency feature pipelines over payment, trip, device, and identity events, ensuring consistency between offline training and online serving.
• Build and operate the real-time model serving infrastructure integrated into the critical trip creation and payment authorization paths under strict latency and availability budgets.
• Design and implement safe, graduated enforcement mechanisms with full audit trails, reversibility, and support for rider appeals.
• Partner cross-functionally with Product, Finance, and Safety to balance revenue protection, rider trust, and compliance as Waymo scales to new markets.

Benefits for this role include: Health, dental, vision, life, disability insurance. Retirement Benefits: 401(k) with company match. Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment. Sick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary). Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks. Baby Bonding Leave: 18 weeks. Holidays: 13 paid days per year.

You have:

• Bachelor's degree in Computer Science, Mathematics, Statistics, or equivalent practical experience.
• 8+ years of software engineering experience building distributed backend systems or production ML systems (Python, Java, Kotlin, C++, Go, or similar).
• Hands-on experience training, evaluating, and deploying machine learning models to production.
• Experience serving models or critical business logic in low-latency, high-availability production environments.
• Strong fundamentals in data modeling, and SQL analytics.

We prefer:

• Experience in fraud prevention, risk management, trust & safety, payments, or another adversarial domain where attackers actively adapt.
• Experience with graph/entity-clustering techniques, rules engines, and anomaly detection alongside traditional ML.
• Proven track record building feature/data pipelines and maintaining consistency between training data and real-time inference.
• Experience serving models or critical business logic in low-latency, high-availability production environments.