Applied Research - Quantitative Finance

Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.

Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.

Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.

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.

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  Environments grounded in point-in-time data, realistic market mechanics, transaction costs, latency, capacity, and operational constraints.

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  Agents that conduct quantitative research, use analytical and execution tools, inspect their evidence, and recover from failed hypotheses.

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  Verifiers and evaluations that test robust out-of-sample behavior across assets, horizons, regimes, and adverse conditions.

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  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.

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.

• 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.

We value publications, open-source systems, benchmarks, and shipped products as evidence of exceptional work. Show us something difficult you built, what failed, and how the evidence changed your approach.