Research Engineer / Scientist, Alignment Science
San Francisco, CAFull-TimeMid-levelSoftware Engineering
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
- 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.
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.
