Research Engineer / Scientist, Robustness

Anthropic

  • San Francisco, CA
  • Permanent
  • Full-time
  • 28 days ago
About AnthropicAnthropic'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 roleThe Robustness Team is part of the , and conducts critical safety research and engineering to ensure AI systems can be deployed safely. As part of Anthropic's broader safeguards organization, we work on both immediate safety challenges and longer-term research initiatives, with projects spanning jailbreak robustness, automated red-teaming, monitoring techniques, and applied threat modeling. We prioritize techniques that will enable the safe deployment of more advanced AI systems (ASL-3 and beyond), taking a pragmatic approach to fundamental AI safety challenges while maintaining strong research rigor.You take a pragmatic approach to running machine learning experiments to help us understand and steer the behavior of powerful AI systems. You care about making AI helpful, honest, and harmless, and are interested in the ways that this could be challenging in the context of human-level capabilities. You could describe yourself as both a scientist and an engineer. You'll both focus on risks from powerful future systems (like those we would designate as ASL-3 or ASL-4 under our Responsible Scaling Policy), as well as better understanding risks occurring today. You will work in collaboration with other teams including Interpretability, Fine-Tuning, and the Frontier Red Team.These papers give a simple overview of the topics the team works on: , , , ,Note: Currently, the team has a preference for candidates who are able to be based in the Bay Area. However, we remain open to any candidate who can travel 25% to the Bay Area.Representative projects:
  • Testing the robustness of our safety techniques by training language models to subvert our safety techniques, and seeing how effective they are at subverting our interventions.
  • Run multi-agent reinforcement learning experiments to test out techniques like AI Debate.
  • Build tooling to efficiently evaluate the effectiveness of novel LLM-generated jailbreaks.
  • Write scripts and prompts to efficiently produce evaluation questions to test models' reasoning abilities in safety-relevant contexts.
  • Contribute ideas, figures, and writing to research papers, blog posts, and talks.
  • Run experiments that feed into key AI safety efforts at Anthropic, like the design and implementation of our Responsible Scaling Policy.
You may be a good fit if you:
  • Have significant software, ML, or research engineering experience
  • Have some experience contributing to empirical AI research projects
  • Have some familiarity with technical AI safety research
  • Prefer fast-moving collaborative projects to extensive solo efforts
  • Pick up slack, even if it goes outside your job description
  • Care about the impacts of AI
Strong candidates may also:
  • Have experience authoring research papers in machine learning, NLP, or AI safety
  • Have experience with LLMs
  • Have experience with reinforcement learning
  • Have experience with Kubernetes clusters and complex shared codebases
The expected salary range for this position is:Annual Salary:$315,000—$560,000 USDLogisticsEducation requirements: We require at least a Bachelor's degree in a related field or equivalent experience.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 differentWe 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. Guidance on Candidates' AI Usage: Learn about for using AI in our application process

Anthropic