Staff+ Software Engineer, Account Abuse (Machine Learning)
AI safety and research company building reliable, interpretable, and steerable AI systems, including the Claude product family and developer platform.
Maintainer signals as of 9/25/2026
Funding history
About Anthropic
Anthropic PBC develops frontier AI systems and deploys them through Claude products and the Claude Platform, with a stated focus on safety, interpretability, and steerability.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will build and operate a feature-computation platform for model training and real-time scoring. You will train, evaluate, deploy, and monitor account-abuse and fraud models. You will automate model development, support safe production rollouts, improve labels, and integrate model decisions with product and platform systems.
Requirements
- Proficiency in Python and SQL
- Experience training and deploying machine-learning models to production
- Experience with batch data pipelines using Spark or Beam
- Experience with workflow schedulers such as Airflow
- Understanding of point-in-time correctness and training-serving skew
- Experience explaining technical tradeoffs to non-technical stakeholders
- Feature-platform experience with Chronon, Feast, or Tecton
- Stream-processing experience with Flink, Beam, Dataflow, Kafka Streams, or similar tools
- Production ML experience in fraud, risk, or ranking
- Experience with tree-based models on tabular data
- Experience with unsupervised, clustering-based, or graph-based detection systems
- Integrity, spam, fraud, or abuse-detection experience
- Experience working with scarce, delayed, or noisy labels
- AutoML or ML-workflow automation experience
Responsibilities
- Build and operate a feature-computation platform for model training and real-time scoring
- Train, evaluate, and deploy account-abuse and fraud-detection models
- Build tooling to automate feature development, training, and evaluation
- Implement backtesting, shadow deployments, staged rollouts, and monitoring
- Work with data scientists and Policy & Enforcement to improve labels
- Partner with product and platform teams to integrate model decisions
Benefits
- Optional equity donation matching
- Generous vacation
- Parental leave
- Flexible working hours