Staff+ Software Engineer, ML Inference Path
Anthropic · San Francisco, CA
About this role
**About Anthropic** Anthropic’s mission is to create reliable, interpretable, and steerable AI systems—safe and beneficial for users and society. **About the role (Safeguards ML Inference Path)** The Safeguards ML Inference Path team designs, builds, and operates the production infrastructure powering Claude’s ML-based safety systems. You’ll collaborate with safety researchers and inference engineers to bring new classifiers and ML defenses into production, owning the research → production transfer for safety technologies that are critical for every Claude model launch. You’ll build for scale—serving thousands of ML classifiers across all requests on the token generation path and across platforms (1P, Bedrock, Vertex, and beyond). **Responsibilities** - Design and build scalable ML infrastructure for real-time safety deployments across classifier and model ecosystems - Build monitoring and observability tools to track classifier performance, data quality, and system health - Collaborate with research teams to productionize safety research (turning experimental techniques into robust, scalable systems) - Optimize inference latency and throughput for real-time safety evaluations while maintaining high reliability - Implement automated testing, deployment, and rollback systems for production safety ML applications - Partner with Safeguards, Security, and Alignment teams to deliver infrastructure that meets safety and production needs - Contribute to internal tools and frameworks that accelerate safety research and deployment **You may be a good fit if you:** - Are proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAX - Understand distributed systems principles and have built systems for high-throughput, low-latency workloads - Have built automated/self-service deployment pipelines and evaluation infrastructure enabling independent classifier/model rollouts - Have implemented A/B testing and experimentation infrastructure for ML systems - Are results-oriented with a bias toward reliability and impact in safety-critical systems - Enjoy collaborating with researchers and translating cutting-edge research into production - Care deeply about AI safety and the societal impacts of your work **Strong candidates may also have experience with:** - 5+ years building production ML infrastructure (ideally in safety-critical domains like fraud detection, content moderation, or risk assessment) - Working with LLMs and modern transformer architectures - Monitoring/alerting systems for model performance and data drift - Trust & safety, fraud prevention, or content moderation domains - Privacy-preserving ML techniques and compliance requirements **Compensation** - Annual Salary: **$320,000 — $485,000 USD** **Logistics** - Minimum education: Bachelor’s degree (or equivalent combination of education, training, and/or experience) - Required field of study: Relevant to the role (via coursework, training, or professional experience) - Minimum years of experience: Correlates with internal job level - Location-based hybrid policy: Expected to be in an office at least **25%** of the time (some roles may require more) - Visa sponsorship: Available, but not guaranteed for every role/candidate; reasonable efforts are made if offered **Apply even if you don’t meet every qualification** Not all strong candidates will match every listed requirement. Anthropic encourages applications from candidates who are interested in the
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