Applied Research - Quantitative Finance
Core
Build environments, evaluations, agents, and post-training methods for quantitative research and systematic decision-making using agentic reinforcement learning.
Role type
Applied Research Scientist (Quantitative Finance & RL)
Builds
Quantitative research environments, trading agents, verifiers, and production feedback loops for systematic decision-making.
Domain
Quantitative Finance / Reinforcement Learning / AI Infrastructure
Deliverable
production ML models
Required skills
Reinforcement Learning, Agent Systems, Post-training methods, Quantitative research, Time-series modeling, Python, Experimental design
Preferred skills
Systematic trading, Market microstructure, Econometrics, Financial simulation
Technologies
Python, RL frameworks, Simulation environments
Responsibilities
Build quantitative-research environments for agents to form hypotheses and run backtests; Design rewards and evaluations to distinguish durable improvement from artifacts; Train long-horizon agents to use data and portfolio tools while respecting risk constraints; Own production loops with monitoring and failure attribution.
Seniority
Mid-Senior, hands-on IC
