Description
Anthropic is at the forefront of AI research, dedicated to developing safe, ethical, and powerful artificial intelligence. Our mission is to ensure that transformative AI systems are aligned with human interests. We are seeking a Research Engineer to join our Pre-training team, responsible for developing the next generation of large language models.
Key Responsibilities
- 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 analyse scientific experiments to advance our understanding of large language models
- Optimise 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 optimisations to high-level model design
Qualifications
- 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
Preferred Experience
- Work on high-performance, large-scale ML systems
- Familiarity with GPUs, Kubernetes, and OS internals
- Experience with language modelling using transformer architectures
- Knowledge of reinforcement learning techniques
- Background in large-scale ETL processes
Sample Projects
- Optimising 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 visualisations of model internals, such as attention patterns
The annual compensation range for this role is $500,000-$850,000 USD.