Staff+ Software Engineer, ML Inference Path

Anthropic•San Francisco, CA

16h•Hybrid

About The Position

The Safeguards ML Inference Path team designs, builds, and operates the production infrastructure that powers Claude's ML based safety systems. We collaborate closely with safety researchers and inference engineers to bring new classifiers and novel classes of ML defenses to production. We own the research → production transfer of new safety technologies that is on the critical path for every Claude model launch. And we build for scale: serving thousands of ML classifiers, for all requests on the token generation path, and for every platform Claude runs on -- 1P, Bedrock, Vertex, and beyond. We’re growing the team and looking for engineers who have deep expertise in productionizing ML 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. And your tooling and infrastructure will be used for every model launch, which are becoming more complex, and more frequent.

Requirements

Proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAX
Understand distributed systems principles and have built systems that handle high-throughput, low-latency workloads
Have built automated or self-service deployment pipelines and eval infrastructure allowing researchers to roll out classifiers and models independently
Implemented A/B testing frameworks and experimentation infrastructure for ML 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

Nice To Haves

Have 5+ years of experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessment
Working with large language models and modern transformer architectures
Developing monitoring and alerting systems for ML model performance and data drift
Experience in trust & safety, fraud prevention, or content moderation domains
Knowledge of privacy-preserving ML techniques and compliance requirements

Responsibilities

Design and build scalable ML infrastructure to support real-time safety deployments across our classifier and model ecosystem
Build monitoring and observability tools to track classifier 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