As a Research Engineer on the Discovery Team, you will work end‑to‑end, identifying and addressing key blockers on the path to scientific AGI. You should be familiar with language model training, evaluation, and inference, comfortable triaging research ideas, diagnosing problems, and enjoy working collaboratively in a highly interdisciplinary environment.
Staff Research Engineer, Discovery Team en Anthropic
Híbrido - San Francisco, CA, United States
Más vacantes en AnthropicSalary
USD 350,000 - 850,000
Requirements
Skills
- 8+ years of ML research experience
- familiarity with large scale language model training, evaluation, and inference pipelines
- obsessively iterating on immediate blockers towards longterm goals
- thriving in collaborative problem solving
- expertise in performance optimization and distributed computing systems
- strong problem‑solving skills and ability to identify technical bottlenecks in complex systems
- capability to translate research concepts into scalable engineering solutions
- track record of shipping ML systems that tackle challenging multi‑step reasoning problems
- expertise with performance optimization for language model inference and training
- experience with computer use automation and agentic AI systems
- history working on reinforcement learning approaches for complex task completion
- knowledge of containerization technologies (Docker, Kubernetes) and cloud deployment at scale
- demonstrated ability to work across multiple domains (language modeling, systems engineering, scientific computing)
- experience with VM/sandboxing/container deployment and large‑scale data processing
- experience working with large scale data problem solving and infrastructure
- published research or practical experience in scientific AI applications or long‑horizon reasoning
Responsibilities
- Working across the full stack to identify and remove bottlenecks preventing progress toward scientific AGI
- Develop approaches to address long‑horizon task completion and complex reasoning challenges essential for scientific discovery
- Scaling research ideas from prototype to production
- Create benchmarks and evaluation frameworks to measure model capabilities in scientific workflows and computer use
- Implement distributed training systems and performance optimizations to support large‑scale model development
Technologies
language modelsdistributed systemsVM/sandboxing/container deploymentlarge scale data pipelinesperformance optimizationdistributed training systemsDockerKubernetescloud deployment at scale
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