About Anthropic
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
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/Research Scientist 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.
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 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
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 modeling using transformer architectures
- Knowledge of reinforcement learning techniques
- Background in large-scale ETL processes
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
Logistics
- Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
- Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
- Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
- Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
How we're different
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research.