[Hiring] Applied Research - Quantitative Finance @Prime Intellect

Role Description

Quantitative finance is a demanding setting for agentic reinforcement learning: observations are noisy and non-stationary, actions have delayed and path-dependent consequences, naive objectives invite exploitation, and a convincing historical result can disappear under costs, constraints, or a new regime. You will build environments, evaluations, agents, and post-training methods for quantitative research and systematic decision-making. We have active programs with quantitative funds and other finance teams. The work may span research agents, market and portfolio simulators, time-series reasoning, execution, risk, and the production feedback loops that connect hypotheses to deployed systems.

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, working directly with customers, users, or research partners while maintaining 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 that distinguish durable improvement from the artifacts of 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.

What you will own

• 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

• 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, and shipped products as evidence of exceptional work.

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