Staff Plus Software Engineer (ML Inference Path)

Join the Safeguards ML Inference Path team at Anthropic, where you'll design, build, and operate the production infrastructure that powers Claude's ML-based safety 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. This role offers comprehensive benefits, a competitive salary, and the opportunity to make a significant impact in the field of AI safety.

• Design and build scalable ML infrastructure to support real-time safety deployments across our classifier and model ecosystem.
• 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.

• Enjoy collaborating with researchers and translating cutting-edge research into production systems
• Are proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAX
• Are results-oriented, with a bias towards reliability and impact in safety-critical systems
• Care deeply about AI safety and the societal impacts of your work
• Have implemented A/B testing frameworks and experimentation infrastructure for ML systems
• 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
• 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
• Working with large language models and modern transformer architectures
• Have 5+ years of experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessment
• 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