Research Scientist/Research Engineer, Reinforcement Learning

As a Research Scientist/Research Engineer working on RL, you'll be at the forefront of applying reinforcement learning to markets. You'll conduct original research and own the systems that turn it into production trading: designing and evaluating policy architectures, reward formulations, and objective horizons with rigorous out-of-sample benchmarking; partnering with trading and research teams to source, integrate, and validate their alpha signals within the RL framework; ensuring simulation fidelity against live trading by modeling market microstructure, fill dynamics, liquidity, and latency; building efficient tooling to store, process, and analyze very large volumes of market and signal data; and communicating findings to technical and trading audiences. This isn't incremental optimization; we're pushing the boundaries of what reinforcement learning can do at scale, where your improvements directly impact live trading.

Other duties as assigned or needed. Skills You’ll Need

5+ years of experience developing reinforcement learning and/or deep learning systems with measurable impact in industry and/or academia

Depth in reinforcement learning, including experience designing reward formulations, policy architectures, and evaluation, and taking RL methods from research into production

Proficiency in Python and/or C++

Familiarity with ML libraries/frameworks such as PyTorch (preferred), TensorFlow, and/or JAX

Strong foundation in mathematics and statistics

PhD or Master's degree in Computer Science, Machine Learning, Robotics (or a related subject)

Strong publication record at ICML, ICLR, AAAI, NeurIPS, CVPR, or equivalent

Ability to thrive in a collaborative, team-oriented environment

Creative thinkers who are driven, self-motivated, and eager to solve challenging problems

Reliable and predictable availability

Excellent written and verbal communication skills in English