Staff+ Software Engineer, Account Abuse (Machine Learning)

San Francisco, CA | New York City, NY · San Francisco · USD 320,000-485,000 / year · Hybrid · Posted Sep 23, 2026

At a glance

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

The Account Abuse team ensures Anthropic's computing capacity is allocated fairly, minimizing resources available to bad actors and preventing them from coming back.
As a software engineer on this team, you will build the machine learning systems that help detect and stop abuse at scale using classical ML on structured and behavioral data.

Build and operate a feature computation platform that serves both model training and real-time scoring, with point-in-time correct training data and low-latency online retrieval
Train, evaluate, and deploy models that detect account-level abuse and fraud, running them both offline and online
Build tooling that automates more of the model development 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 training / serving skew, drift, and adversarial adaptation
Work with our data scientists and our Policy & Enforcement team to improve label coverage and quality
Partner with product and platform teams to gather signals and integrate model decisions with minimal impact on their systems' latency, stability, or overall architecture

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
Bachelor’s degree or an equivalent combination of education, training, and/or experience
A field of study relevant to the role as demonstrated through coursework, training, or professional experience

Experience building or operating a feature platform such as Chronon, Feast, or Tecton
Experience with stream processing engines such as Flink, Beam / Dataflow, or Kafka Streams
Experience training ML models in a production setting with demanding serving requirements, such as fraud, risk, or ranking
Experience with tree-based models on tabular data
Experience building unsupervised, clustering-based or graph-based detection systems to surface coordinated account abuse
Experience in integrity, spam, fraud, or abuse detection
Experience working with scarce, delayed, or noisy labels
Experience with AutoML or other approaches to automating the ML workflow

Location-based hybrid policy: currently, we expect all staff to be in one of our offices at least 25% of the time, though some roles may require more time in our offices.