Research Engineer, Frontier Red Team (CBRN, Biosecurity)

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.

We’re building a team that will research and mitigate extreme risks from future models.

This team will intensively red-team models to test the most significant risks they might be capable of in areas such as biosecurity, cybersecurity risks, or autonomy. We believe that clear demonstrations can significantly advance technical research and mitigations, as well as identify effective policy interventions to promote and incentivize safety.

As part of this team, you will lead research to baseline current models and test whether future frontier capabilities could cause significant harm. Day-to-day, you may decide you need to finetune a model to see whether it becomes superhuman in an eval you’ve designed; whiteboard a threat model with a national security expert; test a new training procedure or how a model uses a tool; or brief government, labs, and other research teams. Our goal is to see the frontier before we get there.

We’re currently hiring for our CBRN workstream, with an emphasis on biosecurity risks (as outlined in our Responsible Scaling Policy). By nature, this team will be an unusual combination of backgrounds. We are particularly looking for people with experience in these domains:

• Biosecurity: You're a computational biologist who's concerned about the implications of AI development. You're an academic who researches biosecurity defense. You have experience modeling biological phenomena or developing advanced threat modeling simulations. 
• Science: You’re an ML researcher who builds agents to augment chemistry or biology research. You’ve built a protein language model and you enjoyed looking through the embedding space. You’re a team lead at an ML-for-drug discovery company. You’ve built software for astronauts or materials scientists.
• Evaluations: You’ve managed a large-scale benchmark development project, in AI or other domains. You have ideas about how AI and ML evaluations can be better.

Do not rule yourself out if you do not fit one of those categories - it’s plausible the people we’re looking for do not fit any of the above! If you think about the most significant upsides and downsides of AI, and you can do good research to get glimpses of what those look like, please consider applying.

Please note: We will only be considering candidates who can be based in the Bay Area for this role. We have a strong preference for candidates who can start ASAP, and ideally by March 2025.

Responsibilities

• Design, run, and analyze scientific experiments to advance our understanding of large language models
• Lead technical design discussions to ensure our infrastructure can support both current needs and future research directions
• Collaborate with other engineers to maintain our evaluations codebase
• Work with external partners to develop novel evaluations to accurately assess the biosecurity implications of our models
• Partner closely with researchers, data scientists, policy experts, and other cross-functional partners to advance Anthropic’s safety mission

You may be a good fit if you

• Have strong software engineering, machine learning, or computational biology experience and some understanding of molecular biology, bioengineering, or bioinformatics
• Take pride in writing clean, well-documented code in Python that others can build upon
• Have a track record of using technical infrastructure to interface effectively with machine learning models
• Have familiarity with prompting and engineering large language models
• Are able to balance research goals with practical engineering constraints
• Have strong problem-solving skills and a results-oriented mindset
• Have excellent communication skills and ability to work in a collaborative environment
• Pick up slack, even if it goes outside your job description
• Prefer fast-moving collaborative projects to extensive solo efforts
• Care about the societal impacts of AI

Strong candidates may also have experience with

• Wet lab experience in molecular biology
• Developing evaluations or benchmarks for large language models
• Previous experie