ML Infrastructure Engineer, Safeguards

Details

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. The Safeguards organization builds and scales the infrastructure that powers AI safety systems. This role focuses on designing and implementing ML infrastructure that powers Claude safety, working at the intersection of machine learning, large-scale distributed systems, and AI safety.

Responsibilities

• Design and build scalable ML infrastructure to support real-time and batch classifier and safety evaluations across the 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

Requirements

• 5+ years of experience building production ML infrastructure, ideally in safety-critical domains (e.g., fraud detection, content moderation, risk assessment)
• Proficient in Python
• Experience with ML frameworks such as PyTorch, TensorFlow, or JAX
• Hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes)
• Understanding of distributed systems principles and experience building systems that handle high-throughput, low-latency workloads
• Experience with data engineering tools and building robust data pipelines (examples in the posting: Spark, Airflow, streaming systems)
• Experience or strong orientation toward reliability, monitoring/observability, and production ML best practices (testing, deployment, rollback)
• Ability to collaborate with researchers to translate cutting-edge research into production systems
• A strong interest in AI safety and the societal impacts of AI

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

Compensation & Logistics

• Annual salary range: $320,000 - $405,000 USD
• Education: Bachelor’s degree in a related field or equivalent experience required
• Location: San Francisco, CA; location-based hybrid policy expecting staff to be in office at least 25% of the time
• Visa sponsorship: Anthropic states they do sponsor visas and retain an immigration lawyer to assist where possible
• Deadline: Applications reviewed on a rolling basis

About Anthropic & Benefits

• Anthropic is a public benefit corporation headquartered in San Francisco focused on steerable, trustworthy AI
• Total compensation packages include equity and benefits; the posting mentions competitive compensation, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office space for collaboration