14 days ago
Base Salary
$350k - $850k/yr
Responsibilities
- Identify and remove full-stack bottlenecks limiting progress toward scientific AGI.
- Develop methods for long-horizon task completion and complex reasoning for scientific discovery.
- Scale research ideas from prototypes into production systems.
- Create benchmarks and evaluation frameworks for scientific workflows and computer use.
- Implement distributed training systems and performance optimizations for large-scale model development.
Requirements
- 8+ years of ML research experience.
- Familiarity with large-scale language-model training, evaluation, and inference pipelines.
- Expertise in performance optimization and distributed computing systems.
- Ability to diagnose technical bottlenecks and translate research concepts into scalable engineering solutions.
- Track record of shipping ML systems addressing challenging multi-step reasoning problems.
- Preferred qualifications include experience with computer-use automation, agentic AI, reinforcement learning, Docker, Kubernetes, cloud deployment, VM or sandboxing environments, large-scale data processing, and scientific AI research or applications.
- Published research or practical experience in scientific AI applications or long-horizon reasoning is a plus.
Benefits
- Annual hybrid policy requiring staff to work from an office at least 25% of the time, with some roles requiring more.
- Visa sponsorship is available, with immigration-lawyer support, subject to role and candidate eligibility.
- Competitive compensation, equity donation matching, generous vacation and parental leave, flexible working hours, and office collaboration space.
- Anthropic is a public benefit corporation focused on beneficial, reliable, interpretable, and steerable AI systems.
Tech Stack
Categories
AI ResearchML Engineering
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.