ML Infrastructure Engineer, Safeguards · Anthropic

Sat, 18 Apr 2026 15:44:06 GMT
Engineering

Anthropic · San Francisco, CA · $320,000-$405,000 USD · hybrid

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.

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

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.