Applied Research, Quantitative Finance
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Job Description
Applied researcher building agents, simulators, evaluations, and post-training methods for quantitative finance. Prime Intellect provides open frontier-AI infrastructure for research teams and developers.
Responsibilities:
- Build environments, evaluations, agents, and post-training methods for quantitative research and systematic decision-making
- Turn workflows into tasks, environments, evaluations, and reward signals
- Run post-training experiments against real baselines and determine whether gains come from the model, data, harness, inference, or infrastructure
- Build quantitative-research environments for hypothesis formation, analysis, backtesting on point-in-time data, result diagnosis, and iterative testing
- Design rewards and evaluations distinguishing durable improvement from look-ahead bias, data leakage, overfitting, favorable execution assumptions, hidden concentration, and tail risk
- Train long-horizon agents to use data, research, coding, simulation, and portfolio tools while preserving provenance and respecting risk constraints
- Build environments with realistic market mechanics, transaction costs, latency, capacity, and operational constraints
- Develop agents that conduct quantitative research, use analytical and execution tools, inspect evidence, and recover from failed hypotheses
- Create verifiers and evaluations for 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
- Work directly with customers, users, or research partners while maintaining a substantive research agenda
- Turn reusable work into shared research, open-source systems, and product capabilities
Requirements:
- Depth in RL, agents, or post-training systems and 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: meaningful baselines, careful trace inspection, clear failure attribution, and willingness to change direction when evidence changes
- Strong Python
- Ability to build artifacts that other researchers and engineers can run, inspect, and extend
- Comfort owning ambiguous problems and keeping partners and teammates current
- Publications, open-source systems, benchmarks, or shipped products as evidence of exceptional work
Benefits:
- Equity incentives
- Flexible Work (San Francisco, New York, London, or remote)
- Visa Sponsorship & relocation support
- Professional Development budget
- Team Off-sites & conference attendance
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