Applied Research - Quantitative Finance

Overview

This role supports the development of an open, end-to-end “superintelligence stack” for post-training at frontier scale—covering compute, environments, evaluations, secure sandboxes, high-performance training, and deployment. Work spans applied research and engineering focused on agentic reinforcement learning for quantitative finance.

In this position, you will build environments, evaluations, agents, and post-training methods for quantitative research and systematic decision-making. Existing programs connect to quantitative funds and other finance teams, with projects that may include research agents, market and portfolio simulators, time-series reasoning, execution, risk, and production feedback loops.

Responsibilities

• Build environments grounded in point-in-time data and realistic market mechanics, including transaction costs, latency, capacity, and operational constraints.
• Design rewards and evaluations that distinguish durable improvement from artifacts such as look-ahead bias, data leakage, overfitting, favorable execution assumptions, or hidden concentration and tail risk.
• Train long-horizon agents that use data, research, coding, simulation, and portfolio tools while preserving provenance, respecting risk constraints, and adapting when market conditions change.
• Create agents that conduct quantitative research, use analytical and execution tools, inspect their evidence, and recover from failed hypotheses.
• Develop verifiers and evaluations to test robust out-of-sample behavior across assets, horizons, regimes, and adverse conditions.
• Build production loops with monitoring, human authority, regression gates, and failure attribution across models, data, simulation, and infrastructure.
• Convert experimental results into reusable research, open-source systems, and product capabilities.
• Maintain an externally facing role by working with customers, users, or research partners while sustaining a substantive research agenda.

Representative Early Problems

• Build a quantitative-research environment where an agent forms a hypothesis, writes and runs analysis, runs a backtest on point-in-time data, diagnoses the result, and decides what to test next.
• Design rewards and evaluations to identify durable improvement versus look-ahead bias, data leakage, overfitting, favorable execution assumptions, or hidden concentration and tail risk.
• Train long-horizon agents to use data, research, coding, simulation, and portfolio tools while preserving provenance, respecting risk constraints, and adapting when market conditions change.

Requirements

• Depth in RL, agents, or post-training systems, with the ability to become fluent in quantitative research.
• Prior work in systematic trading, market microstructure, time-series modeling, econometrics, or financial simulation is useful but not required.
• Hands-on experience building and debugging model-training, post-training, agent, evaluation, or closely adjacent experimental loops.
• Strong experimental judgment, including:
  • meaningful baselines
  • careful trace inspection
  • clear failure attribution
  • willingness to change direction when evidence changes
• Strong Python skills and the ability to build artifacts that other researchers and engineers can run, inspect, and extend.
• Comfort owning ambiguous problems and keeping partners and teammates current as issues are resolved.

Evidence of Exceptional Work

Preferred evidence includes publications, open-source systems, benchmarks, and shipped products. Demonstrate something difficult you built, what failed, and how the evidence changed your approach.

Compensation & Benefits

• Cash compensation range: $150–300k + equity incentives
• Flexible work: San Francisco, New York, London, or remote
• Visa sponsorship & relocation support
• Professional development budget
• Team off-sites & conference attendance

Location

San Francisco, New York, London, or remote