Anthropic· AI Research & Engineering· San Francisco, CA
ML Infrastructure Engineer, Safeguards
Classified Tasks (10)
Automate 0%Augment 80%Human-Only 20%
Augment (8)
AI assists, human decides
Design and build scalable ML infrastructure to support real-time and batch classifier and safety evaluations across our model ecosystem
technical
Build monitoring and observability tools to track model performance, data quality, and system health for safety-critical applications
operational
Optimize inference latency and throughput for real-time safety evaluations while maintaining high reliability standards
technical
Implement automated testing, deployment, and rollback systems for ML models in production safety applications
operational
Contribute to the development of internal tools and frameworks that accelerate safety research and deployment
technical
Build and scale the critical infrastructure that powers AI safety systems
technical
Develop platforms and tools that enable safeguards to operate reliably at scale
technical
Design and implement ML infrastructure that powers Claude safety
technical
Human-Only (2)
Requires human judgment
Collaborate with research teams to productionize safety research and translate experimental safety techniques into robust, scalable systems
leadership
Partner with Safeguards, Security, and Alignment teams to understand requirements and deliver infrastructure that meets safety and production needs
communication
Job description
About Anthropic 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. About the role We are seeking a Machine Learning Infrastructure Engineer to join our Safeguards organization, where you'll build and scale the critical infrastructure that powers our AI safety systems. You'll work at the intersection of machine learning, large-scale distributed systems, and AI safety, developing the platforms and tools that enable our safeguards to operate reliably at scale. As part of the Safeguards team, you'll design and implement ML infrastructure that powers Claude safety. Your work will directly contribute to making AI systems more trustworthy and aligned with human values, ensuring our models operate safely as they become more capable. Responsibilities: Design and build scalable ML infrastructure to support real-time and batch classifier and safety evaluations across our model ecosystem Build monitoring and observability tools to track model performance, data quality, and system health for safety-critical applications Collaborate with research teams to productionize safety research, translating experimental safety techniques into robust, scalable systems Optimize inference latency and throughput for real-time safety evaluations while maintaining high reliability standards Implement automated testing, deployment, and rollback systems for ML models in production safety applications Partner with Safeguards, Security, and Alignment teams to understand requirements and deliver infrastructure that meets safety and production needs Contribute to the development of internal tools and frameworks that accelerate safety research and deployment You may be a good fit if you: Have 5+ years of experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessment Are proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAX Have hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes) Understand distributed systems principles and have built systems that handle high-throughput, low-latency workloads Have experience with data engineering tools and building robust data pipelines (e.g., Spark, Airflow, streaming systems) Are results-oriented, with a bias towards reliability and impact in safety-critical systems Enjoy collaborating with researchers and translating cutting-edge research into production systems Care deeply about AI safety and the societal impacts of your work Strong candidates may have experience with: Working with large language models and modern transformer architectures Implementing A/B testing frameworks and experimentation infrastructure for ML systems Developing monitoring and alerting systems for ML model performance and data drift Building automated labeling systems and human-in-the-loop workflows Experience in trust & safety, fraud prevention, or content moderation domains Knowledge of privacy-preserving ML techniques and compliance requirements Contributing to open-source ML infrastructure projects Deadline to apply: None. Applications will be reviewed on a rolling basis. <div class="