Careers - Staff Applied Machine Learning Engineer - Fraud & Abuse

Block

Quick summary

Work type
On-site
Location
San Francisco, CA
Salary
$276,800–$415,200 / yr
Posted
5 days ago
Nearby
99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $221k
This role $346k
$153k most similar roles pay here $443k

This role pays more than 99% of similar roles. Most pay $183,752–$259,212 — the shaded band above. At the midpoint, this role pays about $346k versus about $221k for comparable roles.

Based on 240 similar postings.

Employer

About Block

Block, Inc. (formerly Square) is a financial technology company operating the Square merchant payments ecosystem, Cash App peer-to-peer payments, TIDAL music streaming, and Bitcoin-focused financial services. Industry: Financial Technology & Payments

Block currently has 138 open roles on FindRole.

Listed pay typically runs $180,000–$270,000 across 61 roles with salary data.

Most-posted roles

View all roles at Block

At a glance

TL;DR · Careers - Staff Applied Machine Learning Engineer - Fraud & Abuse

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As a Staff Applied Machine Learning Engineer on the Fraud & Abuse team, you will design and operate production ML systems to mitigate payment fraud, account takeover, identity abuse, merchant risk, scams, and other adversarial activities. Your daily tasks include building real-time and batch decisioning systems that integrate various signals like behavioral data, graphs, devices, networks, and third-party inputs for low-latency model serving. You will manage the entire production lifecycle, ensuring reliable decisions through monitoring, safe rollouts, incident response, and feedback loops while collaborating with ML modelers, risk analysts, and compliance teams to balance fraud prevention and customer access. The role requires deep expertise in fraud/risk domains, strong production ML judgment, and experience with technologies such as Python, TensorFlow, Kafka, and cloud infrastructure.

Skills

Python TensorFlow PyTorch Kubernetes SQL XGBoost LightGBM Apache Kafka feature stores workflow orchestration model-serving systems Cloud infrastructure data warehouses observability monitoring

What you'll do

  • Build and operate real-time and batch ML decisioning systems for fraud prevention.
  • Integrate various signals into low-latency model serving and decision APIs.
  • Own the production lifecycle of risk decisions including monitoring and incident response.
  • Develop feedback loops and AI-assisted workflows for triage and investigation support.
  • Partner with cross-functional teams to balance fraud losses and customer access.

What we're looking for

  • 12+ years of experience 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.
  • Strong judgment in feature pipelines, model serving, evaluation, monitoring, low-latency integration, safe rollout, and incident response.
  • Sound judgment regarding 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.

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