Staff Applied Machine Learning Engineer - Fraud & Abuse

12+ years building and operating production software and ML systems for business-critical products.

Deep expertise in fraud/risk domains such as payment fraud, identity/account integrity, merchant or marketplace risk, scams, trust & safety, abuse prevention, or compliance decisioning.

Strong production ML judgment across feature pipelines, model serving, evaluation, monitoring, low-latency integration, safe rollout, and incident response.

Sound judgment around false-positive tradeoffs, noisy labels, adversarial behavior, customer harm, and cross-functional decisions.

Experience using AI-assisted engineering tools with appropriate verification, testing, and review for high-stakes systems.

Experience with graph-based fraud detection, behavioral sequence models, embeddings, entity resolution, anomaly detection, or human-in-the-loop review.

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.