Research Engineer / Research Scientist, Pretraining (AI)

TL;DR

Research Engineer / Research Scientist (AI): Developing the next generation of large language models with an accent on multimodal capabilities. Focus on model architecture, algorithms, data processing, and optimizing and scaling training infrastructure.

Location: Hybrid (Zürich, Switzerland). Expected to be in the office at least 25% of the time. Visa sponsorship is considered.

Company

Anthropic is a public benefit corporation focused on creating reliable, interpretable, and steerable AI systems for the benefit of society.

What you will do

• Conduct research and implement solutions in 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 understanding of large language models.
• Optimize and scale 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.

Requirements

• Bachelor's degree 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 deep learning frameworks.
• Experience with high-performance, large-scale ML systems, particularly in language modeling.
• Familiarity with ML Accelerators, Kubernetes, and large-scale data processing.
• Strong problem-solving and excellent communication skills.

Nice to have

• Significant software engineering experience.
• Ability to balance research goals with practical engineering constraints.
• Enjoy pair programming and collaborative work.
• Eager to learn more about machine learning research.
• Enthusiasm for AI safety and general progress.

Culture & Benefits

• Work as a single cohesive team on large-scale AI research efforts, prioritizing impact on steerable, trustworthy AI.
• Approach AI research as an empirical science, similar to physics and biology.
• Collaborative environment with frequent research discussions.
• Offer competitive compensation and benefits, with optional equity donation matching.
• Provide generous vacation and parental leave.
• Flexible working hours and a collaborative office space.