应用研究实习生，主动智能与客户世界模型（博士/研究生合作项目）
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
**团队:** Apollo — Block 应用研究与开发
**地点:** 远程（美国/加拿大）
**时长:** 2026 年秋季/冬季实习 — 8 个月，灵活开始时间 2026 年 9 月
**级别:** 研究生（硕士或博士，实习结束后返回原专业）
##### **关于 Apollo**
Apollo 领导 Block 构建 **客户世界模型（CWM）**：一个持续演进的客户目标、上下文、历史、限制和未来需求的表示。
CWM 为 Block 生态系统中的 **主动智能** 提供支持。客户不再需要在产品中寻找功能，而是由智能观察他们的世界，理解什么重要，预测接下来会发生什么，并代表他们采取行动。
我们认为下一代 AI 产品不会由聊天界面或孤立的代理来定义。它们将由丰富的世界模型定义，这些模型使系统能够对客户不断变化的状态进行推理，做出更好的决策，并从结果中持续学习。Apollo 设计、原型设计并指导这一智能层的开发。
##### **关于该职位**
我们正在招聘一小批研究生研究实习生，帮助构建主动智能的基础。
这不是传统的实习。你将全程负责一个研究问题：提出问题、开发方法、运行实验、发表研究成果，并在成功时将你的工作部署到数百万客户和卖家使用的生产系统中。
你将在表示学习、基础模型、强化学习、因果推理、代理系统和产品智能的交叉领域工作。目标不仅是构建更智能的模型，而是构建能够更深入理解客户并随时间推移做出更好决策的系统。
过去的实习生在几个月内就将生产系统投入了使用，并在同一年发表了他们的研究成果。
##### **你将参与的工作**
根据你的兴趣和 Apollo 的路线图，你将专注于以下一个或多个领域：
**客户世界模型**
从事件流、财务活动、操作信号和行为数据中构建丰富的客户表示。
示例包括：
- 长期客户历史的表示学习
- 基于事件的基础模型
- 跨结构化、序列和图数据的多模态客户表示
- 长期客户理解的记忆架构
**主动智能**
查看英文原文
**Team:** Apollo — Block Applied R&D
**Location:** Remote (US / Canada)
**Duration:** Fall/Winter 2026 co-op — 8 months, flexible start September 2026
**Level:** Graduate student (MS or PhD, returning to your program after the co-op)
##### **About Apollo**
Apollo leads Block's efforts to build the **Customer World Model (CWM)**: a continuously evolving representation of each customer's goals, context, history, constraints, and likely future needs.
The CWM powers **proactive intelligence** across Block's ecosystem. Instead of customers navigating products in search of features, intelligence observes their world, understands what matters, anticipates what comes next, and initiates actions on their behalf.
We believe the next generation of AI products will not be defined by chat interfaces or isolated agents. They will be defined by rich world models that enable systems to reason over a customer's evolving state, make better decisions, and learn continuously from outcomes. Apollo designs, prototypes, and guides the development of this intelligence layer.
##### **About the role**
We're hiring a small cohort of graduate research interns to help build the foundations of proactive intelligence.
This is not a traditional internship. You'll own a research problem end-to-end: framing the question, developing methods, running experiments, publishing findings, and, when successful, shipping your work into production systems used by millions of customers and sellers.
You'll work at the intersection of representation learning, foundation models, reinforcement learning, causal reasoning, agentic systems, and product intelligence. The goal is not simply to build smarter models, but to build systems that develop a deeper understanding of customers and use that understanding to make better decisions over time.
Past interns have shipped production systems within months and published their work in the same year.
##### **What you'll work on**
Depending on your interests and Apollo's roadmap, you'll focus on one or more of the following areas:
**Customer World Models**
Building rich representations of customers from event streams, financial activity, operational signals, and behavioral data.
Examples include:
- Representation learning over long-horizon customer histories
- Event-based foundation models
- Multi-modal customer representations spanning structured, sequential, and graph data
- Memory architectures for long-term customer understanding
**Proactive Intelligence**
Developing systems that can anticipate customer needs and initiate helpful actions before being asked.
Examples include:
- Opportunity detection and next-best-action systems
- Long-horizon planning and decision-making
- Preference and goal inference
- Learning when intervention creates value versus friction
**Agentic Decision Systems**
Building agents that reason over customer world models and take actions in real environments.
Examples include:
- Tool use and planning
- Multi-step reasoning over customer state
- Autonomous workflow execution
- Recovery and adaptation under uncertainty
**Learning from Feedback Loops**
Developing methods that allow intelligence to improve continuously from real-world outcomes.
Examples include:
- Reinforcement learning from customer and product feedback
- Reward modeling and preference learning
- Counterfactual evaluation
- Credit assignment over long decision horizons
**Evaluation and Measurement**
Building evaluation frameworks that predict real-world performance, trust, and customer value.
Examples include:
- Simulated customer environments
- Longitudinal evaluation
- Decision quality metrics
- Safety and reliability benchmarks
##### **What we're looking for**
We're looking for researchers interested in building systems that understand people, learn from experience, and improve over time.
**Required**
- Currently enrolled in an MS or PhD program in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or a related field, and returning to that program after the co-op.
- Strong foundations in modern machine learning, including deep learning, optimization, representation learning, and foundation models.
- Experience conducting independent research and translating ideas into working systems.
- Fluency in Python and experience with PyTorch, JAX, or similar frameworks.
- Evidence of research excellence through publications, open-source contributions, technical leadership, or equivalent work.
**Nice to have**
- Experience with large language models and agentic systems.
- Experience with reinforcement learning, reward modeling, or sequential decision-making.
- Experience with representation learning for structured, temporal, or graph data.
- Familiarity with large-scale training and production ML systems.
- Interest in building AI systems that directly affect customer outcomes.
##### **What you'll get**
- Direct mentorship from researchers working on the future of proactive intelligence at Block.
- Access to large-scale datasets, modern infrastructure, frontier models, and substantial compute resources.
- Opportunities to publish and contribute to open-source projects.
- A chance to shape foundational technology that could power the next generation of Block products.
- Exposure to both scientific research and product deployment, with a clear path from idea to impact.
**Application Guidelines**
Candidates may submit up to 9 active applications within a 60-day period. Reapplications to the same role are accepted 90 days after a previous application has been reviewed.
**Use of AI in Our Hiring Process**
We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws.
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_Every benefit we offer is designed with one goal: empowering you to do the best work of your career while building the life you want. Remote work, medical insurance, flexible time off, retirement savings plans, and modern family planning are just some of our offering._ _[Check out our other benefits at Block.](https://block.xyz/employee-benefits)_
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