about 3 hours ago
Base Salary
$320k - $485k/yr
Responsibilities
- Develop and maintain core infrastructure and features in Claude.ai and the Electron-based desktop application
- Use Rust, Swift, and C++ to connect Claude with macOS and Windows capabilities
- Optimize application performance, memory usage, and startup time across platforms
- Design and build update mechanisms and deployment pipelines
- Create monitoring tools for application performance and user experience
- Explore how advanced language models can augment users’ work on desktop computers
Requirements
- 5+ years of software engineering experience with a strong emphasis on desktop application development
- Practical experience with Electron and a deep understanding of its architecture, or comparable experience with technologies such as Chromium
- Strong JavaScript and TypeScript skills and experience with modern frontend frameworks
- Experience developing native modules in C++ and understanding macOS and/or Windows platform-specific technologies and development patterns
- Experience with cross-platform application packaging, code signing, and distribution
- Product-focused approach to building robust, scalable, and easy-to-use solutions
- Strong candidates may also have experience with operating systems, browsers, deeply OS-integrated software, AI/ML models, cross-platform development strategies, IPC, or desktop application security
- Bachelor’s degree in a relevant field or an equivalent combination of education, training, and experience
Benefits
- Hybrid work policy requiring staff to be in an office at least 25% of the time
- Visa sponsorship and immigration-lawyer support when applicable
- Competitive benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office collaboration space
Tech Stack
About Anthropic
We're an AI research company that builds reliable, interpretable, and steerable AI systems. Our first product is Claude, an AI assistant for tasks at any scale. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.