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Applied Research - Quantitative Finance

Prime Intellect· Applied Research
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San Francisco, New York, Remote, London$150k - $300kFull-timePosted 5d ago

About the role

Role focused on building environments, evaluations, agents, and post-training methods for quantitative research and systematic decision-making in a frontier AI/fintech context. Externally facing research role with collaboration with customers, users, and research partners.

Responsibilities

  • We turn a workflow into tasks, environments, evaluations, and reward signals, run post-training experiments against real baselines, and deploy what works.
  • When a result looks good, we find out whether the gain came from the model or from the data, harness, inference, or infrastructure.
  • The reusable parts become shared research, open-source systems, and product capabilities.
  • Every Applied Research role is externally facing. Most researchers work directly with customers, users, or research partners while maintaining a substantive research agenda.
  • 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 that distinguish durable improvement from 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.
  • Environments grounded in point-in-time data, realistic market mechanics, transaction costs, latency, capacity, and operational constraints.
  • Agents that conduct quantitative research, use analytical and execution tools, inspect their evidence, and recover from failed hypotheses.
  • Verifiers and evaluations that test robust out-of-sample behavior across assets, horizons, regimes, and adverse conditions.
  • Production loops with monitoring, human authority, regression gates, and failure attribution across models, data, simulation, and infrastructure, producing methods that transfer to other noisy, high-stakes decision systems.

Qualifications

  • We are looking first for depth in RL, agents, or post-training systems and 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: meaningful baselines, careful trace inspection, clear failure attribution, and a willingness to change direction when the evidence changes.
  • Strong Python 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 you resolve them.
  • Evidence of exceptional work (publications, open-source systems, benchmarks, shipped products) as demonstration of capabilities.

Skills

Required:RL / Agents / Post-training systemsPythonExperimental design and analysis

Benefits

  • Cash Compensation Range of $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
Last updated Oct 21, 2026. Interested? Apply on the company site.

About Prime Intellect

Commoditizing Compute & Intelligence

Prime Intellect democratizes AI development at scale. Our platform makes it easy to find global compute resources and train state-of-the-art models through distributed training across clusters. Collectively own the resulting open AI innovations, from language models to scientific breakthroughs.

AI developmentbessemer-ai-infrastructure-2026compute resourcesdistributed trainingmodelsreinforcement-learning-platformsscience
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