Staff+ Software Engineer, ML Inference Path
$320k - $485k • San Francisco, CA
Posted 1mo ago
Job Location
San Francisco, CA
Tech Stack
Remote Work Policy
On-site
Categories
Machine Learning Engineer
About the job
The Safeguards ML Inference Path team designs, builds, and operates the production infrastructure that powers Claude's ML-based safety systems. This role involves collaborating closely with safety researchers and inference engineers to bring new classifiers and novel classes of ML defenses to production, owning the research-to-production transfer of new safety technologies critical for every Claude model launch. The team builds for scale, serving thousands of ML classifiers for all requests on the token generation path and for every platform Claude runs on. Engineers will work at the intersection of machine learning, large-scale distributed systems, and AI safety, developing platforms and tools that enable safeguards to operate reliably at scale, with tooling and infrastructure used for every model launch.
Responsibilities
- Design and build scalable ML infrastructure for real-time safety deployments.
- Develop monitoring and observability tools for classifier performance, data quality, and system health.
- Productionize safety research by translating experimental techniques into robust, scalable systems.
- Optimize inference latency and throughput for real-time safety evaluations.
- Implement automated testing, deployment, and rollback systems for ML models in production safety applications.
- Partner with Safeguards, Security, and Alignment teams to deliver infrastructure meeting safety and production needs.
- Contribute to internal tools and frameworks that accelerate safety research and deployment.
Requirements
- Proficiency in Python and experience with ML frameworks like PyTorch, TensorFlow, or JAX.
- Understanding of distributed systems principles and experience building high-throughput, low-latency systems.
- Experience building automated deployment pipelines and eval infrastructure for independent model rollouts.
- Implementation of A/B testing frameworks and experimentation infrastructure for ML systems.
- Results-oriented with a bias towards reliability and impact in safety-critical systems.
- Enjoy collaborating with researchers and translating research into production systems.
- Deep care for AI safety and the societal impacts of work.
Benefits
- Annual compensation range: $320,000 - $485,000 USD
- Visa sponsorship available for some roles.