Research Engineer / Scientist, Pre-training

Anthropic•San Francisco, NY

3d•Hybrid

About The Position

Anthropic is seeking a Research Engineer to join its Pre-training team and help develop the next generation of large language models. The role combines cutting-edge research with practical engineering to advance safe, steerable, and trustworthy AI systems. The work spans the full stack, from low-level optimizations to high-level model design, and includes research, experimentation, training infrastructure, and developer tooling. Team members collaborate on large initiatives and may independently lead smaller research projects.

Requirements

Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field.
Strong software engineering skills and a proven track record of building complex systems.
Expertise in Python and experience with deep learning frameworks; PyTorch is preferred.
Familiarity with large-scale machine learning, particularly 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 collaboratively.
Care about the societal impacts of your work.
Bachelor’s degree or equivalent combination of education, training, and/or experience; the field of study must be relevant to the role, as demonstrated through coursework, training, or professional experience.

Nice To Haves

Experience working on high-performance, large-scale machine learning systems.
Familiarity with GPUs, Kubernetes, and operating system internals.
Experience with language modeling using transformer architectures.
Knowledge of reinforcement learning techniques.
Background in large-scale ETL processes.

Responsibilities

Conduct research and implement solutions in model architecture, algorithms, data processing, and optimizer development.
Independently lead small research projects and collaborate with team members on larger initiatives.
Design, run, and analyze scientific experiments to advance understanding of large language models.
Optimize and scale training infrastructure to improve efficiency and reliability.
Develop and improve developer tooling to enhance team productivity.
Contribute across the stack, from low-level optimizations to high-level model design.