Staff Applied ML Engineer - Fraud & Abuse (Remote)

Block; San Francisco (CA); On-site; USD 276,800 - 415,200; Full time

Benefits offered by this job

Remote work

Medical insurance

Flexible time off

Retirement savings plans

Job summary

Block is seeking a Staff Applied Machine Learning Engineer focused on Fraud & Abuse in San Francisco. You will design, build, and operate production ML systems aimed at reducing payment fraud and identity abuse.

The role requires strong production engineering fundamentals and deep expertise in fraud/risk domains. A flexible work environment and a market-based compensation structure are offered, with a salary range of $276,800 to $415,200 depending on the zone in the U.S.

Qualifications

• 12+ years building production software and ML systems.
• Deep expertise in fraud, identity, and risk domains.
• Strong production ML judgment across model serving.

Responsibilities

• Build and operate real-time ML decision systems for fraud prevention.
• Develop feedback loops for incident response and triage.
• Partner with modelers and analysts to balance fraud losses with customer access.

Skills

Production software and ML systems

Fraud/risk domains expertise

Low-latency integration

AI-assisted operations

Tools

Python

TensorFlow

Kafka

Job description

Block is seeking a Staff Applied Machine Learning Engineer focused on Fraud & Abuse in San Francisco. You will design, build, and operate production ML systems aimed at reducing payment fraud and identity abuse.

The role requires strong production engineering fundamentals and deep expertise in fraud/risk domains. A flexible work environment and a market-based compensation structure are offered, with a salary range of $276,800 to $415,200 depending on the zone in the U.S.