Anthropic
Research Engineer / Research Scientist, Tokens
About this role
Anthropic seeks a Research Engineer to build safe, scalable AI systems by working across infrastructure, experiments, and tooling. You'll contribute to large-scale ML projects while maintaining a focus on reliable and interpretable AI development.
What you'll do
- Develop and maintain distributed ML infrastructure and clusters for large-scale training
- Design and run scientific experiments to evaluate ML systems and optimizations
- Improve system throughput, efficiency, and reliability for research workloads
- Build developer tools and improve the research engineering workflow
- Work on transformer model optimization and language model research projects
- Collaborate on data processing pipelines and ETL systems
What they're looking for
- Software engineering and systems programming
- High-performance ML systems and distributed training
- PyTorch, Kubernetes, GPU optimization, or OS internals
- Language model and transformer architecture knowledge
- Reinforcement learning experience
- Large-scale data processing and ETL
- Problem-solving with flexibility and impact focus
- Pair programming and collaborative development
Benefits
- $350,000 - $500,000 annual salary
- Hybrid work arrangement (minimum 25% office time)
- Visa sponsorship available with immigration legal support
- Opportunity to work on AI safety and societal impact
- Collaborative research environment with pair programming culture
- Multiple office locations (NYC, Seattle, San Francisco)
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Anthropic
Anthropic builds Claude, an AI assistant, and is hiring for engineering roles across infrastructure, data systems, and security that support both AI research operations and the company's internal technology needs. The company seeks infrastructure engineers, systems integrators, data scientists, and security specialists to build production-scale systems for training data pipelines, financial operations, developer productivity measurement, research infrastructure, and server firmware security.
- Website
- anthropic.com
Likely interview questions
- Walk us through your experience building or optimizing large-scale ML systems. What was the biggest bottleneck you encountered and how did you address it?
- Describe a time when you had to pair program or collaborate closely with researchers on a complex technical problem. How did you ensure you understood the research context?