Note: The job is a remote job and is open to candidates in USA. Prime Intellect is hiring an Applied Researcher focused on quantitative finance. The role builds environments, evaluations, agents, and post-training methods for quantitative research and systematic decision-making, and works with finance teams to connect research to deployed systems.
Responsibilities
- You will build environments, evaluations, agents, and post-training methods for quantitative research and systematic decision-making
- 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
- 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 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
- 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
Skills
- We are looking first for depth in RL, agents, or post-training systems and the ability to become fluent in quantitative research
- 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
- Prior work in systematic trading, market microstructure, time-series modeling, econometrics, or financial simulation is useful but not required
Benefits
- Equity incentives
- Flexible work (San Francisco, New York, London, or remote)
- Visa sponsorship & relocation support
- Professional Development budget
- Team Off-sites & conference attendance
Company Overview
Company H1B Sponsorship