Applied Research, Quantitative Finance

This is a fully remote position, open to applicants in California.

📋 Description

• Develop environments, evaluations, agents, and post-training methodologies for quantitative research and systematic decision-making.

• Transform workflows into tasks, environments, evaluations, and reward signals.

• Conduct post-training experiments against real baselines to assess whether improvements arise from the model, data, harness, inference, or infrastructure.

• Create quantitative-research environments for hypothesis generation, analysis, backtesting on point-in-time data, result diagnosis, and iterative testing.

• Design rewards and evaluations that differentiate between sustainable improvement and issues such as look-ahead bias, data leakage, overfitting, favorable execution assumptions, hidden concentration, and tail risk.

• Train long-horizon agents to utilize data, research, coding, simulation, and portfolio tools while maintaining provenance and adhering to risk constraints.

• Construct environments that replicate realistic market mechanics, transaction costs, latency, capacity, and operational constraints.

• Develop agents capable of conducting quantitative research, utilizing analytical and execution tools, examining evidence, and recovering from failed hypotheses.

• Create verifiers and evaluations that ensure robust out-of-sample behavior across various assets, timeframes, regimes, and challenging conditions.

• Establish production loops that include monitoring, human oversight, regression gates, and failure attribution across models, data, simulation, and infrastructure.

• Collaborate directly with customers, users, or research partners while maintaining a substantive research agenda.

• Convert reusable work into shared research, open-source systems, and product capabilities.

⛳️ Requirements

• Expertise in reinforcement learning (RL), agents, or post-training systems, with the capacity to gain fluency in quantitative research.

• Previous experience in systematic trading, market microstructure, time-series modeling, econometrics, or financial simulation is advantageous but not mandatory.

• Practical experience in building and debugging model-training, post-training, agent, evaluation, or related experimental loops.

• Strong experimental judgment, including the establishment of meaningful baselines, thorough trace inspection, clear failure attribution, and readiness to pivot when evidence shifts.

• Proficiency in Python.

• Ability to create artifacts that can be executed, inspected, and extended by other researchers and engineers.

• Comfort with managing ambiguous problems while keeping partners and teammates informed.

• Publications, open-source contributions, benchmarks, or shipped products that demonstrate exceptional work.

🏝️ Benefits

• Equity incentives.

• Flexible work arrangements (San Francisco, New York, London, or remote).

• Visa sponsorship and relocation support.

• Professional development budget.

• Team off-sites and conference attendance.