研究工程师,宇宙
Research Engineer, Universes
关于 Anthropic
Anthropic 的使命是创造可靠、可解释且可引导的 AI 系统。我们希望 AI 对我们的用户以及整个社会都是安全且有益的。我们的团队是一支快速发展的由致力于研究、工程、政策专家和商业领袖组成的团队,共同构建有益的 AI 系统。
关于团队
Research 中的 Universes 团队负责训练 AI 模型在超现实环境中执行复杂、困难、长周期的代理任务。我们设计并实现新颖的训练环境,这些环境远超当前模型的能力——在这些环境中,模型学习如何处理模糊性、应对中断、在长时间交互中保持上下文,并在开放性场景中做出判断。
关于职位
我们正在寻找 Research Engineers 来帮助我们构建下一代用于强大且安全的代理 AI 的训练环境。
该职位结合了研究和工程职责,需要你既能够实现新的方法,又能为研究方向做出贡献。你将从事强化学习的基础研究,设计推动技术前沿的训练环境和方法,并构建衡量真实能力的评估体系。
职责:
- 构建下一代代理环境
- 构建严谨的评估体系以衡量真实能力
- 与研究和基础设施团队协作,将环境部署到生产训练中
- 在研究和生产 ML 堆栈之间快速调试和迭代
- 通过技术讨论和协作解决问题来贡献研究文化
如果你符合以下条件,可能会是一个合适的人选:
- 以成果为导向——你关注的是结果,而不是活动
- 具有高度的自主性
- 有良好的研究品味或高级技术经验,能够在复杂问题空间中识别真正重要的问题
- 能够在研究探索与工程实现之间取得平衡
- 对 AI 的潜在影响充满热情,并致力于开发安全且有益的系统
- 能够适应不确定性和快速变化的环境
- 具备扎实的软件工程技能,能够构建稳健的基础设施
- 喜欢结对编程(我们非常推崇结对编程!)
优秀的候选人可能还具备以下一项或多项:
- 在大型语言模型训练、微调或评估方面有行业经验
查看英文原文
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 Team
The Universes team within Research is responsible for training AI models to perform complex, difficult, long-horizon agentic tasks in ultra-realistic settings. We design and implement novel training environments that go far beyond what models can do today — environments where models learn to navigate ambiguity, handle interruptions, maintain context over extended interactions, and exercise judgment in open-ended scenarios.
About the Role
We're looking for Research Engineers to help us build the next generation of training environments for capable and safe agentic AI.
This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to research direction. You'll work on fundamental research in reinforcement learning, designing training environments and methodologies that push the state of the art, and building evaluations that measure genuine capability.
Responsibilities:
- Build the next generation of agentic environments
- Build rigorous evaluations that measure real capability
- Collaborate across research and infrastructure teams to ship environments into production training
- Debug and iterate rapidly across research and production ML stacks
- Contribute to research culture through technical discussions and collaborative problem-solving
You may be a good fit if you:
- Are highly impact-driven — you care about outcomes, not activity
- Operate with high agency
- Have good research taste or senior technical experience, demonstrating good judgment in identifying what actually matters in complex problem spaces
- Can balance research exploration with engineering implementation
- Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems
- Are comfortable with uncertainty and adapt quickly as the landscape shifts
- Have strong software engineering skills and can build robust infrastructure
- Enjoy pair programming (we love to pair!)
Strong candidates may also have one or more of the following:
- Have industry experience with large language model training, fine-tuning or evaluation
- Have industry experience building RL environments, simulation systems, or large-scale ML infrastructure
- Senior experience in a relevant technical field even if transitioning domains
- Deep expertise in sandboxing, containerization, VM infrastructure, or distributed systems
- Published influential work in relevant ML areas
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:
$500,000—$850,000 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.