Anthropic
Software Engineer, Account Abuse (Machine Learning)
About this role
Build machine learning systems to detect and prevent account abuse at Anthropic's platform. You'll develop full-stack ML solutions for fraud detection, working on feature pipelines, model training, deployment, and monitoring in a production environment where accuracy directly impacts legitimate users.
What you'll do
- Design and operate feature computation platforms for both training and real-time scoring with low-latency retrieval
- Train, evaluate, and deploy abuse detection models for offline and online use cases
- Build automation tooling for the ML development lifecycle, leveraging Claude to accelerate workflows
- Establish backtesting, shadow deployment, and staged rollout practices with drift and skew monitoring
- Collaborate with data scientists and policy teams to improve labeling coverage and quality
- Integrate model decisions with product and platform teams while maintaining system latency and stability
What they're looking for
- Python and SQL
- Machine learning model training and production deployment
- Batch processing frameworks (Spark, Beam) and workflow orchestration (Airflow)
- Point-in-time correctness and training/serving skew prevention
- Feature platform architecture (Chronon, Feast, Tecton preferred)
- Tree-based models on tabular data
- Stream processing (Flink, Kafka Streams)
- Fraud, risk, or abuse detection experience
Benefits
- Work on safety-critical systems with real societal impact
- Access to Claude for accelerating ML workflows
- Hybrid work policy with 25% office time minimum
- Visa sponsorship available
- Opportunity to shape AI safety and integrity practices
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Anthropic
Anthropic builds Claude, an AI assistant, and is hiring for engineering roles across infrastructure, data systems, and security that support both AI research operations and the company's internal technology needs. The company seeks infrastructure engineers, systems integrators, data scientists, and security specialists to build production-scale systems for training data pipelines, financial operations, developer productivity measurement, research infrastructure, and server firmware security.
- Website
- anthropic.com
Likely interview questions
- Describe a time you trained and deployed a production ML model where false positives had significant real-world consequences. How did you measure and optimize for precision?
- How would you design a feature platform that prevents training/serving skew while supporting both batch training and low-latency online scoring?