Staff ML Engineer: Account Abuse & Fraud Detection

Anthropic Limited; New York (NY); Hybrid; USD 320,000 - 485,000; Full time

Job summary

Anthropic Limited is seeking a software engineer for the Account Abuse team to build ML systems that detect and stop abuse at scale. You will develop a feature computation platform for training and real-time scoring, ensuring precise, timely data and low-latency retrieval.

The role emphasizes understanding adversarial behavior, training and deploying models in production, and using Claude to accelerate development and evaluation.

Qualifications

• Proficiency in Python and SQL.
• Experience training machine learning models and deploying them to production.
• Experience building data pipelines with a batch processing engine (e.g., Spark, Beam) and a workflow scheduler (e.g., Airflow).
• Working understanding of point-in-time correctness and training/serving skew, and how to prevent both.
• Strong communication skills and ability to explain technical tradeoffs to non-technical stakeholders.

Responsibilities

• Build and operate a feature computation platform that serves model training and real-time scoring with low latency.
• Train, evaluate, and deploy models that detect account-level abuse and fraud, offline and online.
• Build tooling to automate model development including using Claude to speed up feature development, training, and evaluation.
• Make backtesting, shadow deployment, and staged rollout the default path to production with monitoring for skew, drift, and adversarial adaptation.
• Work with data scientists and Policy & Enforcement to improve label coverage and quality.
• Partner with product and platform teams to gather signals and integrate model decisions with minimal latency impact.

Skills

Python

SQL

Point-in-time correctness

Communication

Education

Bachelor’s degree or equivalent

Tools

Spark

Beam

Airflow

Job description

Anthropic Limited is seeking a software engineer for the Account Abuse team to build ML systems that detect and stop abuse at scale. You will develop a feature computation platform for training and real-time scoring, ensuring precise, timely data and low-latency retrieval.

The role emphasizes understanding adversarial behavior, training and deploying models in production, and using Claude to accelerate development and evaluation.