Research Engineer / Scientist, Alignment Science

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 role: You want to build and run elegant and thorough 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. As a Research Engineer on Alignment Science, you'll contribute to exploratory experimental research on AI safety, with a focus on risks from powerful future systems (like those we would designate as ASL-3 or ASL-4 under our Responsible Scaling Policy), often in collaboration with other teams including Interpretability, Fine-Tuning, and the Frontier Red Team. Our blog provides an overview of topics that the Alignment Science team is either currently exploring or has previously explored. Our current topics of focus include... Scalable Oversight: Developing techniques to keep highly capable models helpful and honest, even as they surpass human-level intelligence in various domains. AI Control: Creating methods to ensure advanced AI systems remain safe and harmless in unfamiliar or adversarial scenarios. Alignment Stress-testing: Creating model organisms of misalignment to improve our empirical understanding of how alignment failures might arise. Automated Alignment Research: Building and aligning a system that can speed up & improve alignment research. Alignment Assessments : Understanding and documenting the highest-stakes and most concerning emerging properties of models through pre-deployment alignment and welfare assessments (see our Claude 4 System Card), misalignment-risk safety cases, and coordination with third-party evaluators. Safeguards Research: Developing robust defenses against adversarial attacks, comprehensive evaluation frameworks for model safety, and automated systems to detect and mitigate potential risks before deployment. Model Welfare: Investigating and addressing potential model welfare, moral status, and related questions. See our program announcement and welfare assessment in the Claude 4 system card for more. Note: For this role, we conduct all interviews in Python and prefer candidates to be based in 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 Candidates need not have: 100% of the skills needed to perform the job Formal certifications or education credentials The annual compensation range for this role is listed below. For sales roles, the range provided is the role's On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $350,000 - $500,000 USD 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 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. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links--visit anthropic.com/careers directly for confirmed position openings. 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. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process Create a Job Alert Interested in building your career at Anthropic? Get future opportunities sent straight to your email. Create alert Apply for this job * indicates a required field First Name* Last Name* Email* Phone Country* Phone* Resume/CV* AttachAttach Dropbox Google Drive Enter manuallyEnter manually Accepted file types: pdf, doc, docx, txt, rtf * * * (Optional) Personal Preferences* How do you pronounce your name? Website Publications (e.g. Google Scholar) URL GitHub URL When is the earliest you would want to start working with us? Do you have any deadlines or timeline considerations we should be aware of? AI Policy for Application* Select... We believe that AI will have a transformative impact on the world, and we're seeking exceptional candidates who collaborate thoughtfully with Claude to realize this vision. At the same time, we want to understand your unique skills, expertise, and perspective through our hiring process. We invite you to review our AI partnership guidelines for candidates and confirm your understanding by selecting "Yes." In a paragraph or two, why do you want to work on AI safety at Anthropic?* Given your understanding of our team's priorities, what are three projects you'd be excited about working on at Anthropic that are aligned with those priorities? (1-2 sentences each)* You can learn about the team's work here: http://alignment.anthropic.com/ Share a link to the piece of work you've done that is most relevant to the Alignment Science team, along with a brief description of the work and its relevance.* What's your ideal breakdown of your time in a working week, in terms of hours or % per week spent on meetings, coding, reading papers, etc.?* In one paragraph, provide an example of something meaningful that you have done in line with your values. Examples could include past work, volunteering, civic engagement, community organizing, donations, family support, etc.* Will you now or will you in the future require employment visa sponsorship to work in the country in which the job you're applying for is located? * Select... Additional Information* Add a cover letter or anything else you want to share. LinkedIn Profile Please ensure to provide either your LinkedIn profile or Resume, we require at least one of the two. Are you open to working in-person in one of our offices 25% of the time?* Select... Are you open to relocation for this role? * Select... What is the address from which you plan on working? If you would need to relocate, please type "relocating". Team Matching * Pre-training -- The Pre-training team trains large language models that are used by our product, alignment, and interpretability teams. Some projects include figuring out the optimal dataset, architecture, hyper-parameters, and scaling and managing large training runs on our cluster. AI Alignment Research -- the Alignment team works to train more aligned (helpful, honest, and harmless) models and does "alignment science" to understand how alignment techniques work and try to extrapolate to uncover and address new failure modes. Reinforcement Learning - Reinforcement Learning is used by a variety of different teams, both for alignment and to teach models to be more capable at specific tasks. Platform - The Platform team builds shared infrastructure used by Anthropic's research and product teams. Areas of ownership include: the inference service that generates predictions from language models; extensive continuous integration and testing infrastructure; several very large supercomputing clusters and the associated tooling. Interpretability -- The Interpretability team investigates what's going on inside large language models -- in a sense, they are trying to reverse engineer the concepts and mechanics from the inscrutable learned weights of these systems. Their goal is to ensure that AI systems are safe by being able to assess whether they're doing what we actually want, all the way down to the individual neurons. Societal Impacts -- Our Societal Impacts team designs and executes experiments that evaluate the capabilities and harms of the technologies we build. They also support the policy team with empirical evidence. Product -- The Product research team trains, evaluates, and improves upon Claude, integrating all of our research techniques to make our AI systems as safe and helpful as possible. Which teams or projects are you most interested in? 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