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.
For this job posting, you can apply to one of two tracks: Research Scientist or Research Engineer.
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 February 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 experience in emerging technology policy, including in biosecurity or AI
Representative projects
- Design and implement a new evaluation to test models for CBRN risks
- Manage a large-scale automated evaluations run across our clusters
- Improve elicitation for an evaluation by integrating biology-specific tools or packages
- Prepare briefing materials to share the results of an evaluation run with external research groups
Candidates need not have
- Previous professional experience in AI Safety
- 100% of the skills needed to perform the job
Deadline to apply: None. Applications will be reviewed on a rolling basis.
The expected salary range for this position is:
Logistics
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 enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
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. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Come work with us!
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.
