Applied Research Intern, Proactive Intelligence & Customer World Models

Block

. Own a research problem end-to-end: frame the question, develop methods, run experiments, publish findings, and potentially ship work into production systems .

Posted 10/31/2026internshipRemote • Canada, United StatesEntry LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in machine learning methodologies, particularly in reinforcement learning, representation learning, and the development of proactive intelligence systems. Proficient in Python and frameworks like PyTorch and JAX, with a strong foundation in translating research into practical applications that enhance customer outcomes.

Highest-signal resume keywords

Machine LearningReinforcement LearningPython ProgrammingRepresentation LearningResearch Excellence

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Deep LearningOptimizationFoundation ModelsReward ModelingSequential Decision-MakingLarge Language ModelsAgentic SystemsEvaluation FrameworksCausal ReasoningGraph Data

Soft Skills

Independent ResearchCollaboration

Tools & Technologies

PyTorchJAXProduction ML Systems

Industry Keywords

Customer OutcomesBehavioral DataEvent StreamsOperational SignalsFinancial Activity

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

Own a research problem end-to-end: frame the question, develop methods, run experiments, publish findings, and potentially ship work into production systems
Build rich customer representations from event streams, financial activity, operational signals, and behavioral data
Develop proactive intelligence systems that anticipate customer needs and initiate helpful actions
Build agents that reason over customer world models and take actions in real environments
Develop methods for reinforcement learning from customer and product feedback, reward modeling, preference learning, counterfactual evaluation, and long-horizon credit assignment
Build evaluation frameworks for real-world performance, trust, and customer value
Collaborate across representation learning, foundation models, reinforcement learning, causal reasoning, agentic systems, and product intelligence

Requirements

What you’ll need

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
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

Benefits

Comp & perks

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
Remote work
Medical insurance
Flexible time off
Retirement savings plans
Modern family planning