Anthropicでは、大規模モデルの研究経験を持つリサーチサイエンティストを募集しています。再帰的自己改善を測定・理解するための評価モデルを設計し、AI研究開発の加速シグナルを分析します。定量的なモデリング能力とAI安全性への深い関心が不可欠です。部門横断的なチームと協力し、研究成果を評価レポートとしてまとめる役割を担います。
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 looking for a Research Scientist who has done hands-on research on large models (pretraining, fine-tuning, RL, evals, or agents scaffolds) and wants to focus on measuring and understanding recursive-self-improvement. You know what the model-development loop looks like from the inside: which signals matter and where the real bottlenecks are. On this team you'll use that judgment to decide what's worth measuring, design the evaluations and models that measure it, and interpret what the results mean for how fast this is moving.
We're hiring at both junior and senior levels. Senior researchers should be comfortable doing hands-on technical work alongside setting research direction.
Identify the signals that track AI R&D acceleration and design the evaluations that measure them
Build quantitative models of capability growth and self-improvement dynamics, grounded in evaluation and telemetry data
Run experiments and evals to test hypotheses about automation and capability
Make opinionated research bets and own the outcome
Write graded assessments of what our measurements show, for internal decision-makers and public reporting
Collaborate with pretraining, RL, economic research, and policy teams
Have done hands-on research on large language models: pretraining, fine-tuning, RL, evals, or agent systems
Have strong quantitative instincts, are comfortable with quantitative modeling and reasoning
Have experience in forecasting, may have published AI forecasting scenarios
Can design an evaluation from a vague question and defend the methodology
Write clearly and calibrate: state confidence, name what would change your conclusion
Are motivated by impact: comfortable with work whose output is graded assessments and system-card sections more often than papers
Care about AI safety and think carefully about where rapid capability growth leads
Trained or RL'd frontier models hands-on
Experience with scaling laws, capability forecasting, or emergent-capability studies
A physics, applied-math, or similarly quantitative background that moved into ML
Written a system card section, capability report, or methodology document that others cite
Experience supervising and correcting AI-written code
Anthropic ECI: our adaptation of Epoch Capabilities Index published in all recent system cards to measure capability acceleration
AI R&D capability assessments in the Claude system cards
When AI Builds Itself: all data in the article comes from our team
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—$850,000 USD
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.
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.
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.
