Research Engineer / Research Scientist, Pre-training

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. About the team We are seeking passionate Research Scientists and Engineers to join our growing Pre-training team in Zurich. We are involved in developing the next generation of large language models. The team primarily focuses on multimodal capabilities: giving LLMs the ability to understand and interact with modalities other than text. 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. Responsibilities In this role you will interact with many parts of the engineering and research stacks. - 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 & Experience We encourage you to apply even if you do not believe you meet every single criterion. Because we focus on so many areas, the team is looking for both experienced engineers and strong researchers, and encourage anyone along the researcher/engineer spectrum to apply. - Degree (BA required, MS or PhD preferred) 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 - Have worked on high-performance, large-scale ML systems, particularly in the context of language modeling - Familiarity with ML Accelerators, Kubernetes, and large-scale data processing - Strong problem-solving skills and a results-oriented mindset - Excellent communication skills and ability to work in a collaborative environment You'll thrive in this role if you - Have significant software engineering experience - Are able to balance research goals with practical engineering constraints - Are happy to 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 - Have ambitious goals for AI safety and general progress in the next few years, and you’re excited to create the best outcomes over the long-term Sample Projects - Optimizing the throughput of novel attention mechanisms - Proposing Transformer variants, and experimentally comparing their performance - Preparing large-scale datasets for model consumption - Scaling distributed training jobs to thousands of accelerators - Designing fault tolerance strategies for training infrastructure - Creating interactive visualizations of model internals, such as attention patterns If you're excited about pushing the boundaries of AI while prioritizing safety and ethics, we want to hear from you! 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 Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position 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. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have en

Bachelor's degree or equivalent (Minimum BA; MS or PhD preferred) in Computer Science, Machine Learning, or a related field. Strong software engineering skills with a 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, results-oriented mindset, and excellent communication in a collaborative environment. Proven ability to balance research goals with practical engineering constraints and to contribute across the stack from low-level optimizations to high-level model design. Ability to lead small research projects independently and work with teams on larger initiatives.

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 understanding of large language models. Optimize and scale training infrastructure to improve efficiency and reliability. Develop and improve development tooling to enhance team productivity. Contribute to the entire stack, from low-level optimizations to high-level model design.