Staff Plus Software Engineer (ML Inference Path)

Anthropic; San Francisco (CA); On-site; USD 180,000 - 280,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Parental leave

Equity

Retirement plan

Wellness stipend

Relocation support

Education stipend

Home office stipend

Meals in office

Job summary

Anthropic is seeking an experienced engineer to design and operate scalable ML infrastructure that powers Claude’s safety systems. You will join the Safeguards ML Inference Path team and collaborate with researchers and inference engineers to productionize safety research, optimize inference latency, and ensure reliability at scale.

You will build monitoring, observability, and automated deployment frameworks, enabling researchers to roll out classifiers and models across platforms while

Qualifications

• Proficiency in Python for ML tooling and production pipelines.
• Experience with ML frameworks (PyTorch, TensorFlow, JAX).
• Strong background in distributed systems and reliability for safety-critical deployments.

Responsibilities

• Design scalable ML infrastructure for real-time safety deployments.
• Build monitoring/observability for classifier performance and data quality.
• Collaborate with research to productionize safety techniques.
• Reduce inference latency while maintaining safety standards.
• Implement automated testing, deployment, and rollback for ML models.
• Work with Safeguards, Security, and Alignment teams on requirements.
• Develop internal tools to accelerate safety research and deployment.

Skills

Python

PyTorch

TensorFlow

JAX

Distributed systems

Reliability

Tools

A/B testing frameworks

Experimentation infra

Job description

• 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
• We own the research → production transfer of new safety technologies that is on the critical path for every Claude model launch. And we build for scale: serving thousands of ML classifiers, for all requests on the token generation path, and for every platform Claude runs on — 1P, Bedrock, Vertex, and beyond
• We’re growing the team and looking for engineers who have deep expertise in productionizing ML 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. And your tooling and infrastructure will be used for every model launch, which are becoming more complex, and more frequent
• 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
• Contribute to the development of internal tools and frameworks that accelerate safety research and deployment

Benefits

• Comprehensive health, dental, and vision insurance for you and your dependents
• Inclusive fertility benefits via Carrot Fertility
• 22 weeks of paid parental leave
• Flexible paid time off and absence policies
• Mental health support for you and your dependents
• Competitive salary and equity packages
• Optional equity donation matching at a 1:1 ratio, up to 25% of your equity grant
• Retirement plans with competitive matching
• Life and income protection plans
• $500/month flexible wellness and time saver stipend
• Commuter benefits
• Annual education stipend
• Home office stipends
• Relocation support for those moving for Anthropic
• Daily meals and snacks in the office

Enjoy collaborating with researchers and translating cutting-edge research into production systemsAre proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAXAre results-oriented, with a bias towards reliability and impact in safety-critical systemsCare deeply about AI safety and the societal impacts of your workHave implemented A/B testing frameworks and experimentation infrastructure for ML systemsUnderstand distributed systems principles and have built systems that handle high-throughput, low-latency workloadsHave built automated or self-service deployment pipelines and eval infrastructure allowing researchers to roll out classifiers and models independentlyNot all strong candidates will meet every single qualification as listedWorking with large language models and modern transformer architecturesHave 5+ years of experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessmentDeveloping monitoring and alerting systems for ML model performance and data driftExperience in trust & safety, fraud prevention, or content moderation domainsKnowledge of privacy-preserving ML techniques and compliance requirements