Staff ML Engineer - Account Abuse Detection

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

Benefits offered by this job

Generous vacation

Parental leave

Flexible working hours

Equity donation matching

Job summary

Anthropic is seeking a software engineer on the Account Abuse team to build ML systems that detect and stop abuse at scale. You will develop a feature computation platform, train and deploy models, and automate model development lifecycles using Claude to accelerate feature work. The role emphasizes production-ready systems, precision, and safe rollout.

The team collaborates with data scientists and policy teams to improve signal coverage while maintaining low latency and robust architecture.

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 and a workflow scheduler.

Responsibilities

• Build and operate a feature computation platform for model training and real-time scoring.
• Train, evaluate, and deploy models that detect account-level abuse and fraud.
• Build tooling to automate the model development lifecycle, including Claude integration.
• Implement backtesting, shadow deployment, and staged rollout with monitoring.
• Collaborate with data scientists and policy teams to improve label quality.
• Partner with product and platform teams to minimize latency impact.

Skills

Python

SQL

ML deployment

Data pipelines

Spark/Beam

Airflow

Point-in-time correctness

Communication

Education

Bachelor's degree

Tools

Chronon

Feast

Tecton

Flink

Dataflow

Kafka Streams

Job description

Anthropic is seeking a software engineer on the Account Abuse team to build ML systems that detect and stop abuse at scale. You will develop a feature computation platform, train and deploy models, and automate model development lifecycles using Claude to accelerate feature work. The role emphasizes production-ready systems, precision, and safe rollout.

The team collaborates with data scientists and policy teams to improve signal coverage while maintaining low latency and robust architecture.