Applied Researcher - Quantitative Finance

What you'll do

• Develop realistic quantitative-research environments, agents, and tools grounded in market data and constraints.
• Design rewards, verifiers, and evaluations to test robust out-of-sample performance and guard against bias, overfitting, and risk.
• Run post-training experiments, diagnose results, and build monitored production feedback loops with human oversight.

What they ask for

• Depth in RL, agents, or post-training systems, with ability to become fluent in quantitative research.
• Hands-on experience building and debugging model-training, post-training, agent, evaluation, or adjacent experimental loops.
• Strong experimental judgment, including careful baselines, trace inspection, and failure attribution.
• Strong Python and ability to build artifacts other researchers and engineers can run, inspect, and extend.

Nice to have

• Prior work in systematic trading, market microstructure, time-series modeling, econometrics, or financial simulation is useful but not required.
• Evidence of exceptional work such as publications, open-source systems, benchmarks, or shipped products.

Pay

$150-300k USD base salary

Tools

Python

AI focus

Develop and post-train long-horizon agents for quantitative research, tool use, backtesting, and systematic decision-making; build evaluations and production feedback loops for these agents.