This role is part of Anthropic’s Safeguards team, responsible for designing, building, and operating the production infrastructure that powers Claude’s ML-based safety systems. Engineers will work at the intersection of machine learning, large‑scale distributed systems, and AI safety to develop platforms and tools that enable safeguards to operate reliably at scale across all Claude models and platforms.
Staff+ Software Engineer, ML Inference Path en Anthropic
Híbrido - San Francisco, CA, USA
Más vacantes en AnthropicSalary
USD 320,000 - 485,000
Requirements
Skills
- Proficient in Python and experienced with ML frameworks such as PyTorch, TensorFlow, or JAX
- Understand distributed systems principles and have built high-throughput, low-latency workloads
- Built automated or self-service deployment pipelines and evaluation infrastructure for researchers to roll out classifiers and models
- Implemented A/B testing frameworks and experimentation infrastructure for ML systems
- Results-oriented with a bias toward 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 societal impacts of work
- 5+ years of experience building production ML infrastructure, ideally in safety-critical domains such as fraud detection, content moderation, or risk assessment
- Experience working with large language models and modern transformer architectures
- Experience 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 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
Technologies
PythonPyTorchTensorFlowJAXLarge language modelsTransformer architecturesA/B testing frameworksMonitoring and alerting systems
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