Research Engineer, Anthropic

• Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field
• Strong software engineering skills with a proven track record of building complex systems
• Expertise in Python and experience with deep learning frameworks (PyTorch preferred)
• Familiarity with large-scale machine learning, particularly in the context of language models
• Ability to balance research goals with practical engineering constraints
• Strong problem-solving skills and a results-oriented mindset
• Excellent communication skills and ability to work in a collaborative environment
• Care about the societal impacts of your work
• Have significant software engineering experience
• Are results-oriented with a bias towards flexibility and impact
• Willingly take on tasks outside your job description to support the team
• Enjoy pair programming and collaborative work
• Are eager to learn more about machine learning research
• Are enthusiastic to work at an organization that functions as a single, cohesive team pursuing large-scale AI research projects
• Are working to align state of the art models with human values and preferences, understand and interpret deep neural networks, or develop new models to support these areas of research
• View research and engineering as two sides of the same coin, and seek to understand all aspects of our research program as well as possible, to maximize the impact of your insights
• Have ambitious goals for AI safety and general progress in the next few years, and you’re working to create the best outcomes over the long-term

Desirable

• We are seeking a Research Engineer to join our Pre-training team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems
• Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development
• Independently lead small research projects while collaborating with team members on larger initiatives
• Design, run, and analyze scientific experiments to advance our understanding of large language models
• Optimize and scale our training infrastructure to improve efficiency and reliability
• Develop and improve dev tooling to enhance team productivity
• Contribute to the entire stack, from low-level optimizations to high-level model design
• Sample Projects:
• Optimizing the throughput of novel attention mechanisms
• Comparing compute efficiency of different Transformer variants
• Preparing large-scale datasets for efficient model consumption
• Scaling distributed training jobs to thousands of GPUs
• Designing fault tolerance strategies for our training infrastructure
• Creating interactive visualizations of model internals, such as attention patterns