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