The opportunity
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.
What you'll do
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
What they're looking for
- Contribute to the development of internal tools and frameworks that accelerate safety research and deployment
- Are 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
