2 months ago
Base Salary
$405k - $485k/yr
Responsibilities
- Define the future of agentic productivity at scale and at the frontier.
- Own the technical strategy and roadmap for the assigned area and translate goals into execution.
- Define quality standards for agent-driven engineering work and uphold them across the company.
- Build and ship the agent runtime platform and related tooling.
- Build agent harnesses and experiment with context management strategies to improve agent performance and correctness.
- Write evaluations to benchmark agent behaviors.
- Collaborate across teams to deliver impact.
- Own infrastructure scalability and reliability and establish operational excellence practices.
Requirements
- 10+ years of experience building and operating large-scale distributed systems.
- 3+ years of experience leading large-scale, complex projects or teams as an engineer or tech lead.
- Significant experience with agents performing technical or knowledge work.
- Experience building scalable platforms.
- Excellent communication skills and comfort supporting internal partners.
- Bachelor’s degree in a relevant field, or an equivalent combination of education, training, and/or experience.
- Experience with container or VM orchestration at scale, working with researchers and engineers, developer productivity or infrastructure, and widely adopted CLI tools and services is preferred.
Benefits
- Annual compensation range of $405,000–$485,000 USD
- Hybrid policy requiring staff to work from an office at least 25% of the time
- Visa sponsorship may be available
- Optional equity donation matching
- Generous vacation and parental leave
- Flexible working hours
- Office space for collaboration
Categories
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.