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Anthropic is seeking a Software Engineer to build internal tools for employee productivity, focusing on fraud, trust & safety, sales, and support teams. The role involves rapid prototyping, collaborating with teams to automate workflows, and maintaining tools for usability and reliability.

As a Product Manager at Anthropic, you will lead the ideation and execution of AI products, working with research teams to translate innovations into viable products. Your role involves rapid prototyping, user insight synthesis, and market analysis to drive product development amid ambiguity.

The role involves optimizing accelerator utilization, collaborating on feature implementation, maintaining AI safety, and building infrastructure for deploying large language models. You'll work with both internal teams and third-party partners to ensure high service performance and contribute innovative ideas towards better AI systems.

The Commercial Counsel will design, negotiate, and manage government contracts while ensuring compliance with federal regulations. This role involves advising cross-functional teams on strategies for selling to the government and enhancing internal processes. The successful candidate will bring significant legal experience, particularly in government contracting and technology transactions.

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.