Staff+ Software Engineer, ML Inference Path
About this role
Engineers who thrive on translating complex research into robust systems and care deeply about the societal impacts of AI will find a strong alignment with this group. Anthropic is expanding its Safeguards ML Inference Path team in San Francisco to design and operate the production infrastructure powering safety systems for Claude. Team members work at the intersection of machine learning, distributed systems, and safety, managing the transfer of new technologies from research to production.
This position exists because safety defenses must operate reliably at scale for thousands of classifiers across multiple platforms like Bedrock and Vertex. Successful candidates bring deep expertise in productionizing ML systems, a strong bias toward reliability, and a collaborative approach to partnering with research teams.
What you will do
- Design and build scalable ML infrastructure to support real-time safety deployments across the 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 by 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.
What you need
- Proficiency in Python alongside hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, or JAX.
- Demonstrated understanding of distributed systems principles with experience building systems that handle high-throughput and low-latency workloads.
- Experience building automated or self-service deployment pipelines and evaluation infrastructure for researchers to roll out classifiers and models independently.
- Prior implementation of A/B testing frameworks and experimentation infrastructure tailored for machine learning systems.
- Strong results-orientation paired with a distinct bias toward reliability and impact in safety-critical systems.
- A Bachelor's degree in a relevant field or an equivalent combination of education, training, and experience.
Nice to have
- 5+ years of experience building production ML infrastructure in safety-critical domains like fraud detection, content moderation, or risk assessment
- Working with large language models and modern transformer architectures
- 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
Benefits
- Competitive compensation and benefits
- Optional equity donation matching
- Generous vacation and parental leave
- Flexible working hours
- Lovely office space in which to collaborate with colleagues
Skills and tech stack
Questions candidates ask
What is the remote work policy?
Anthropic operates under a location-based hybrid policy. Staff are expected to be in one of the offices at least 25% of the time, though some roles may require more time in the office.
What is the compensation range for the role?
The annual base salary range for this position is $320,000 to $485,000 USD.
Does Anthropic provide visa sponsorship?
Yes, visa sponsorship is available. If an offer is made, the company makes every reasonable effort to secure a visa with the assistance of a retained immigration lawyer.