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Salary: USD 150,000 - 300,000 / annual
Prime Intellect is building the open superintelligence stack—infrastructure for frontier AI labs to conduct post-training at scale. The company has raised $150M from Founders Fund, Radical Ventures, NVIDIA, and prominent AI operators including Andrej Karpathy and leaders from Perplexity, Harvey, Cognition, OpenAI, and others.
You will lead applied research in quantitative finance, a uniquely demanding domain for agentic reinforcement learning. Financial markets present noisy, non-stationary observations; actions carry delayed and path-dependent consequences; naive objectives invite exploitation; and historical results often fail to persist under real costs, constraints, and regime changes.
Your work will span building environments, evaluations, agents, and post-training methods for quantitative research and systematic decision-making. Prime Intellect has active programs with quantitative funds and finance teams. The scope includes research agents, market and portfolio simulators, time-series reasoning, execution strategies, risk management, and production feedback loops connecting hypotheses to deployed systems.
Applied Research at Prime means turning workflows into tasks, environments, evaluations, and reward signals; running post-training experiments against real baselines; and deploying what works. You will validate whether gains come from the model, data, harness, inference, or infrastructure. Reusable insights become shared research, open-source systems, and product capabilities.
Representative early problems include: building quantitative-research environments where agents form hypotheses, write and run analysis, backtest on point-in-time data, diagnose results, and decide what to test next; designing rewards and evaluations that distinguish durable improvement from look-ahead bias, data leakage, overfitting, and hidden tail risk; training long-horizon agents to use data, research, coding, simulation, and portfolio tools while preserving provenance, respecting risk constraints, and adapting to market changes.
You will own environments grounded in point-in-time data with realistic market mechanics, transaction costs, latency, and operational constraints; agents that conduct quantitative research, use analytical and execution tools, inspect evidence, and recover from failed hypotheses; verifiers and evaluations testing robust out-of-sample behavior across assets, horizons, regimes, and adverse conditions; and production loops with monitoring, human authority, regression gates, and failure attribution.
Every Applied Research role is externally facing. Most researchers work directly with customers, users, or research partners while maintaining a substantive research agenda.
REQUIREMENTS:
- Depth in reinforcement learning, agents, or post-training systems, with ability to become fluent in quantitative research
- Prior work in systematic trading, market microstructure, time-series modeling, econometrics, or financial simulation (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, willingness to change direction based on evidence
- Strong Python and 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 valued: publications, open-source systems, benchmarks, shipped products
