Research Scientist Research Engineer Reinforcement Learning
Jump Trading is a global trading firm where traders, engineers, and researchers develop trading strategies, models, infrastructure, and systems across asset classes and time horizons.
About Jump Trading
Jump Trading is a global trading firm focused on research-driven trading and the engineering of scalable models, tools, infrastructure, and execution systems. Its operations combine trading, technology, AI/ML, and quantitative research, and it also runs research and talent programs including conference travel grants and a fellowship program.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will apply reinforcement learning to financial markets by designing and evaluating policy architectures, reward formulations, and objective horizons through rigorous out-of-sample benchmarking. You will integrate and validate alpha signals, model market microstructure and trading dynamics for realistic simulation, and build tooling for large-scale market and signal data. You will communicate findings to technical and trading audiences and take research systems into production trading.
Requirements
- 5+ years of experience developing reinforcement learning or deep learning systems with measurable impact
- Expertise in reinforcement learning reward formulations policy architectures and evaluation
- Experience taking reinforcement learning methods from research into production
- Proficiency in Python or C++
- Familiarity with PyTorch TensorFlow 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 an equivalent venue
- Excellent written and verbal communication skills in English
- Reliable and predictable availability
Responsibilities
- Design and evaluate reinforcement learning policy architectures
- Develop reward formulations and objective horizons
- Benchmark models out of sample
- Source integrate and validate alpha signals within reinforcement learning frameworks
- Model market microstructure fill dynamics liquidity and latency
- Ensure simulation fidelity against live trading
- Build tooling to store process and analyze large volumes of market and signal data
- Communicate research findings to technical and trading audiences
- Develop reinforcement learning research into production trading systems
Benefits
- Discretionary bonus eligibility
- Medical dental and vision insurance
- HSA FSA and Dependent Care options
- Employer-paid group term life and AD&D insurance
- Voluntary life and AD&D insurance
- Paid vacation and holidays
- Retirement plan with employer match
- Paid parental leave
- Wellness programs