Staff Research Engineer, Discovery - AI Scientist Path

Anthropic Limited; San Francisco (CA); Híbrido; USD 350.000 - 850.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Equity donation matching

Generous vacation and parental leave

Flexible working hours

Office space in SF

Descripción de la vacante

Anthropic Limited is seeking a Research Engineer to join our team and advance scalable AI systems from research to production. You will work end-to-end on language model training, evaluation, and inference, addressing bottlenecks and driving long-horizon reasoning tasks.

The role emphasizes distributed training, performance optimization, and collaboration across disciplines. Hybrid work with office presence in San Francisco is expected.

Formación

• 8+ years of ML research experience with deep exposure to language models.
• Familiarity with training, evaluation, and inference pipelines for large-scale models.
• Ability to triage research ideas, diagnose bottlenecks, and collaborate across teams.
• Strong background in distributed systems and performance optimization.
• Experience translating research concepts into scalable engineering solutions.

Responsabilidades

• Identify and remove bottlenecks across the full stack toward scientific AGI.
• Develop methods for long-horizon task completion and complex reasoning.
• Scale research ideas from prototype to production.
• Create benchmarks and evaluation frameworks for model capabilities.
• Implement distributed training systems and performance optimizations.

Conocimientos

Language model training

Evaluation pipelines

Distributed computing

Performance optimization

Problem solving

Cross-team collaboration

Educación

Bachelor's degree

Herramientas

Docker

Kubernetes

VM/sandboxing

Cloud deployment

Descripción del empleo

Anthropic Limited is seeking a Research Engineer to join our team and advance scalable AI systems from research to production. You will work end-to-end on language model training, evaluation, and inference, addressing bottlenecks and driving long-horizon reasoning tasks.

The role emphasizes distributed training, performance optimization, and collaboration across disciplines. Hybrid work with office presence in San Francisco is expected.