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The Applied AI Finetuning Engineer will develop tailored LLM solutions for clients, collaborating with various teams to integrate AI into their products, execute finetuning projects, and serve as a technical advisor on best practices for deployment.

The role involves developing core components of APIs to access large language models, integrating tools for billing and email, optimizing performance, and collaborating with researchers to enhance user interfaces. Strong coding skills and 5+ years of API and frontend development experience are required, with a focus on building scalable and robust solutions.

As a Research Scientist on the Interpretability team, you will develop methods for understanding large language models through the reverse engineering of algorithms within neural networks. This involves designing and conducting robust experiments to achieve mechanistic interpretability, aimed at improving the safety and performance of AI systems.

As a Research Engineer in Alignment Science, you will conduct machine learning experiments to understand AI safety. You will collaborate with teams to explore experimental research, build tooling for evaluating AI systems, and contribute to research papers and projects that focus on ensuring safe AI development.

Develop innovative interfaces and APIs for Claude.AI, optimizing performance and user experience. Collaborate with researchers to enhance models based on user feedback. Implement tools to monitor performance and contribute to novel interaction paradigms. Emphasize a user-centric approach to product development.

As a Performance Engineer at Anthropic, you will identify and solve large-scale systems problems to optimize performance in machine learning applications. Responsibilities include implementing efficient algorithms, debugging network issues, and designing fault-tolerant distributed systems while working collaboratively in a results-oriented environment.

In this role, you will manage compute capacity planning and allocation, coordinate workload migrations, and ensure smooth transitions between compute environments, collaborating closely with engineering and research teams to optimize resource utilization and operationalize strategic compute plans.