Applied Research, Quantitative Finance

🕒 October 19

🏄 California – Remote

🏄 California – Remote

💵 $150k - $300k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

💸 Financial Planning and Analysis (FP&A)

👻 Ghost score 4%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Prime Intellect

WebsiteLinkedInAll Job Openings

1 - 10 employees

🤖 Artificial Intelligence

☁️ SaaS

Artificial Intelligence • SaaS • Cloud Computing

Prime Intellect is a company focused on democratizing AI development by providing scalable and decentralized computing resources for training models. Their platform allows users to find and share global compute resources, enabling the training of state-of-the-art models through distributed clusters. They promote the collective ownership of AI innovations, including language and scientific models. Prime Intellect also offers a range of GPU options to facilitate affordable and efficient model training. They aim to advance decentralized training research and open-source AI development on a global scale.

📋 Description

• Build environments, evaluations, agents, and post-training methods for quantitative research and systematic decision-making • Turn workflows into tasks, environments, evaluations, and reward signals • Run post-training experiments against real baselines and determine whether gains come from the model, data, harness, inference, or infrastructure • Build quantitative-research environments for hypothesis formation, analysis, backtesting on point-in-time data, result diagnosis, and iterative testing • Design rewards and evaluations distinguishing durable improvement from look-ahead bias, data leakage, overfitting, favorable execution assumptions, hidden concentration, and tail risk • Train long-horizon agents to use data, research, coding, simulation, and portfolio tools while preserving provenance and respecting risk constraints • Build environments with realistic market mechanics, transaction costs, latency, capacity, and operational constraints • Develop agents that conduct quantitative research, use analytical and execution tools, inspect evidence, and recover from failed hypotheses • Create verifiers and evaluations for robust out-of-sample behavior across assets, horizons, regimes, and adverse conditions • Build production loops with monitoring, human authority, regression gates, and failure attribution across models, data, simulation, and infrastructure • Work directly with customers, users, or research partners while maintaining a substantive research agenda • Turn reusable work into shared research, open-source systems, and product capabilities

🎯 Requirements

• Depth in RL, agents, or post-training systems and 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 willingness to change direction when evidence changes • Strong Python • Ability to build artifacts that other researchers and engineers can run, inspect, and extend • Comfort owning ambiguous problems and keeping partners and teammates current • Publications, open-source systems, benchmarks, or shipped products as evidence of exceptional work

🏖️ Benefits

• Equity incentives • Flexible Work (San Francisco, New York, London, or remote) • Visa Sponsorship & relocation support • Professional Development budget • Team Off-sites & conference attendance

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