{"id":3840163,"url":"https://alion.io/job/anthropic-research-scientist-discovery-engineering-applied-sciences","title":"Research Scientist, Discovery (Engineering & Applied Sciences)","company":{"id":5,"name":"Anthropic","domain":"anthropic.com","url":"https://alion.io/company/anthropic","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":80,"open_postings":159,"ghost_share":0,"stale_share":0.811,"repost_share":0.006,"time_to_fill_p50_days":54,"computed_at":"2026-11-06T05:58:05Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":500000,"max":null,"currency":"USD","period":"year","gross":null,"usd_annual":500000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anthropic","optional":false},{"name":"Claude","optional":false},{"name":"Machine Learning","optional":false},{"name":"AI Agents","optional":true},{"name":"Interpretability","optional":true},{"name":"JAX","optional":true},{"name":"Multimodal AI","optional":true},{"name":"Pre-training","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"Reinforcement Learning","optional":true}],"status":"live","first_seen_at":"2026-11-05T18:52:27Z","employer_posted_date":"2026-11-05","last_verified_at":"2026-11-06T16:17:49Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About Anthropic\nAnthropic’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.\nAbout the role\nThe Discovery team at Anthropic is organized around the north star goal of building an AI scientist - a system capable of solving the long-term reasoning challenges and basic capabilities necessary to push the scientific and technological frontier.\nWe're expanding our work on engineering and the applied sciences. Our goal is to contribute to training Claude to invent, design, and engineer across every discipline and scale, reliably and safely: new manufacturing and experimental techniques, processes, materials, devices, instruments, machines, energy systems, and factories. We want this work to accelerate science and technology and benefit people widely.\nTo achieve these goals, we're seeking exceptional machine learning researchers with deep expertise in training and evaluating frontier models. In this role, you'll work end to end across the model stack, developing training environments, rewards, data, and evaluations that measure and improve Claude's performance on complex engineering and scientific tasks, and carrying what works into production training runs. You'll collaborate closely with researchers, engineers, and partners across Anthropic and industry to build AI systems that can engage in all phases of research and development, and that learn from real-world feedback. A background in engineering or the sciences is a plus but not required.\nKey responsibilities\nDesign, build, and study reinforcement learning environments, rewards, and training data that improve Claude's capabilities in engineering and the applied sciences\nCreate benchmarks and evaluation frameworks that measure model performance on realistic engineering and scientific workflows\nDevelop simulation-based training environments and verifiers, working with domain experts to validate them against real-world data\nDevelop approaches to long-horizon task completion, multi-agent collaboration, and complex reasoning essential for real engineering and research work\nResearch how models can learn efficiently from experiments and real-world feedback\nStudy how environments and rewards behave in large-scale training, including robustness, calibration, and generalization, so that training produces models that are both capable and well aligned\nScale research ideas from prototype to production training runs\nCollaborate with domain experts and industry partners to build and test what Claude invents, designs, and engineers, and turn that feedback into training and evaluation signal\nHelp chart how Claude’s engineering capabilities reach real-world use: where they matter most, what stands in the way of adoption, and how that should shape research priorities\nMinimum qualifications\nProficiency in Python and hands-on experience with a modern machine learning framework such as PyTorch or JAX\nHands-on experience training or evaluating machine learning models\nTrack record of machine learning research: forming hypotheses, designing and running experiments, and drawing sound conclusions from the results\nAbility to implement your own research ideas and diagnose problems in complex systems\nAbility to learn unfamiliar technical fields quickly and work productively with experts in them\nClear communication and a collaborative approach to solving problems\nExcitement about improving Claude’s scientific capabilities and the downstream impact on science and technology\nCommitment to developing AI that is safe and beneficial\nPreferred qualifications\nExperience with reinforcement learning and pre-training on large language models, including environment and reward design\nFamiliarity with large-scale language model training, evaluation, and inference pipelines\nPublished research or practical experience in AI for science or engineering, or in long-horizon reasoning\nA degree or work experience in an engineering or applied science discipline, such as mechanical, electrical, materials, chemical, aerospace, systems, or manufacturing engineering, applied physics, or computational simulation\nExperience building simulators, surrogate models, or simulation-based environments and verifiers, and calibrating and validating them against real-world data\nExperience with large-scale data pipelines and technical or multimodal datasets\nDemonstrated ability to work across multiple domains, such as language modeling, systems engineering, and scientific computing\nThe annual compensation range for this role is listed below. \nFor 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.\nAnnual Salary:\n$500,000—$850,000 USD\nLogistics\nMinimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\nRequired field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience\nMinimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\nLocation-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.\nVisa 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.\nWe 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.\nYour 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.\nHow we're different\nWe 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.\nThe 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.\nCome work with us!\nAnthropic 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. 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