Staff ML Engineer - Account Abuse & Fraud Detection

Fdetimes; New York (NY); Híbrido; USD 320.000 - 485.000; Jornada completa

Descripción de la vacante

Anthropic is seeking a software engineer for the Account Abuse team to build ML systems that detect and stop abuse at scale. You will work across model training, productionization, and evaluation, leveraging Claude to accelerate development and iteration.

This role prioritizes robust production systems, precise measurement, and safe rollout to minimize false positives that could lock out legitimate customers; collaboration with data scientists and cross-functional partners is essential.

Formación

• Proficiency in Python and SQL.
• Experience training ML models and deploying them to production.
• Experience building data pipelines with Spark, Beam, and a workflow scheduler like Airflow.
• Understanding of point-in-time correctness and training/serving skew.
• Strong communication skills and ability to explain technical tradeoffs to non-technical stakeholders.

Responsabilidades

• Build and operate a feature computation platform for 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 accelerate the ML lifecycle, 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 and drift.
• Collaborate with data scientists and the Policy & Enforcement team to improve label coverage and quality.
• Partner with product and platform teams to gather signals and integrate model decisions with minimal latency impact.

Conocimientos

Python

SQL

ML model training

production deployment

point-in-time correctness

communication

Educación

Bachelor's degree or equivalent

Herramientas

Spark

Beam

Airflow

Descripción del empleo

Anthropic is seeking a software engineer for the Account Abuse team to build ML systems that detect and stop abuse at scale. You will work across model training, productionization, and evaluation, leveraging Claude to accelerate development and iteration.

This role prioritizes robust production systems, precise measurement, and safe rollout to minimize false positives that could lock out legitimate customers; collaboration with data scientists and cross-functional partners is essential.