Staff Applied Machine Learning Engineer - Fraud & Abuse

The Role

As a Staff Applied Machine Learning Engineer focused on Fraud & Abuse, you will design, build, and operate production ML decision systems that reduce payment fraud, account takeover, identity abuse, merchant and marketplace risk, scams, and other adversarial activity across Block. The team optimizes for reliable decisions, safe deployment, and measurable customer outcomes — preserving access for good customers while reducing fraudulent, abusive, or unsafe activity. You should be comfortable owning production systems end to end: data contracts, low-latency inference, batch scoring, feature quality, online/offline consistency, model deployment, monitoring, incident response, rollback, and outcome feedback loops. The work combines large-scale ML decisioning with AI-assisted operations: surfacing evidence, simulating controls, accelerating triage, and improving feedback loops while preserving human judgment in high-stakes decisions. You will work closely with ML modelers, product engineers, risk analysts, compliance partners, and operations teams to respond quickly to evolving abuse patterns without creating unnecessary friction or harm for legitimate customers.

You Will

• Build and operate real-time and batch ML decisioning systems for payment fraud, scams, identity and account integrity,

merchant and marketplace risk, and abuse prevention.

• Integrate behavioral,

graph, device, network, event-stream, and third-party signals into low-latency model serving, decision APIs, and product controls.

• Own the production lifecycle for risk decisions,

including data contracts, feature quality, online/offline consistency, monitoring, drift detection, safe rollout, rollback, and incident response.

• Develop feedback loops and verified AI-assisted workflows for triage,

investigation support, alert clustering, graph exploration, simulation, and post-incident learning.

• Partner with modelers,

analysts, product, compliance, and operations to balance fraud losses, customer access, false positives, product velocity, support burden, and long-term trust.

Qualifications

• 12+ years of experience in applied machine learning or related fields.
• Strong programming skills in Python or similar languages.
• Experience with large-scale ML systems deployment.
• Knowledge of fraud detection techniques and adversarial machine learning.
• Familiarity with cloud infrastructure such as AWS or GCP.
• Excellent communication skills and ability to work cross-functionally.

Preferred Skills

• Experience with real-time decisioning systems.
• Knowledge of graph analytics.
• Familiarity with AI-assisted operations tools.

Location

Remote / Bay Area

Employment Type

Full-time

Experience Level

Senior / Staff level

Salary Range Estimate

Over $120k USD annually based on seniority level and role complexity.