Head of Quantitative Research

Protogon Research; United States; On-site; USD 250,000 - 420,000; Full time

Benefits offered by this job

Competitive compensation

Meaningful equity

Medical coverage

401(k) and HSA

Job summary

Protogon Research in San Diego, CA seeks an executive Head of Quantitative Research to own trading P&L and lead a high-caliber AI/ML and quant dev team. You will shape model development, risk controls, and capital allocation while expanding market reach and instrument coverage.

You will be deeply hands-on from day one, guiding research, execution, and real-world impact with a bias toward durable edge and measurable performance in live markets.

Qualifications

• PhD preferred or equivalent depth in quantitative research.
• Fluent in Python and modern ML techniques such as deep learning, sequence models, RL, and time-series modeling.
• Experience designing models and experiments, not only deploying existing ones.

Responsibilities

• Own P&L, risk, and capital allocation with clear performance attribution.
• Lead technical organization including ML engineers and quant developers.
• Own research agenda from hypothesis through deployment into live trading.

Skills

Quantitative Research

Python

Deep Learning

Time-series Modeling

Reinforcement Learning

Model Deployment

Education

PhD in quantitative discipline

Job description

About the job Head of Quantitative Research

Protogon Research is an elite team based in San Diego, CA, backed by top VCs including OVO Fund, Zelda Ventures, and others. Led by serial entrepreneur Rafael Cosman, co-founder of Archblock and the TrueFi DeFi protocol, we design autonomous AI systems and deploy them directly into financial markets through proprietary trading. This is where research meets the real world, and where models must perform, adapt, and improve continuously.

Financial markets provide clear feedback and real consequences, which gives us several key advantages:

• Clear feedback: progress is measurable, and weaknesses surface immediately, forcing our systems to move beyond simple pattern recognition or emulating human behavior toward deeper understanding and super-human performance.
• Long-term focus: unlike traditional quant funds, we’re optimizing for the long-term success of our AI technology, not short-term profits.
• Original work: unlike much of the AI industry, we’re not layering a thin wrapper over existing models. Our work is original, proprietary, and defensible.

We’re seeking an executive-level Head of Quantitative Research to own Protogon’s trading P&L and lead the elite technical organization that produces it: model development, research direction, risk, capital allocation, and execution.

You will lead our AI research direction, as well as how our models connect to markets to generate revenue. We trade digital assets today and intend to expand into additional instruments and markets, with this role leading that expansion. Crypto experience is welcome but not required. You will report to the CEO and lead our technical organization, including our AI/ML and quant dev team. You will be deeply hands-on from day one and will thoughtfully lead and grow your team. We work primarily in person and are looking for candidates based in or willing to relocate to the San Diego area.

Responsibilities and Expectations

Your first priority is P&L. Everything else in this list exists to move that number: the models we build, the risk we take, and the discipline with which we execute. You will have an excellent team reporting to you, but at a small AI lab like ours we expect everyone to be elbows-deep in the models, the execution, and the results.

1. P&L Ownership, Risk & Capital Allocation Own trading performance and hit the targets we set together. Own the constraints our systems operate inside: position sizing, leverage, exposure and concentration limits, and drawdown protocols. Build the attribution that makes performance clear, decomposing returns into model alpha, market exposure, execution quality, financing, and fees, and explaining divergence between simulation and live results with precision.
2. Technical Leadership Lead our technical organization, including our machine learning and software engineers. Set the modeling roadmap, prioritizing it against where capital is actually at work, and hold the team to the standard that live results demand. Own hiring, onboarding, and development in partnership with company leadership, and grow the quantitative and trading capability the book will require at scale.
3. Quantitative Research & Model Development Own the research agenda and the models it produces, from hypothesis and experiment design through deployment into live trading. The architecture is ours to define. Improve performance, robustness, and adaptability across changing market conditions, with a bias toward measurable real-world impact over novelty.
4. Research Velocity & Systems: Raise the rate at which good ideas reach production. Direct the development of core ML infrastructure, data pipelines, training workflows, evaluation tooling, and simulation fidelity, so that models are reliable, observable, and honest about what they will do in live markets.
5. Execution & Trading Operations: Own realized execution quality: venue selection and routing across fragmented liquidity, fee tiers and maker/taker economics, slippage, and transaction cost analysis, alongside margin and collateral management, settlement, custody, and counterparty exposure. Institutionalize the operating layer through monitoring, reconciliation, and tested kill switches.

Who You Are

• Quantitative Researcher Responsible for Results: You bring roughly 8-12 years in quantitative research or applied ML from a systematic fund, proprietary trading firm, or crypto market maker. We care far more about whether your models have carried real capital and you answered for the outcome than about years on a resume. You have made hard calls under pressure about whether an underperforming strategy was broken or simply in a bad regime.
• Deep Modeling Foundation: You have personally designed models and experiments rather than only deploying existing ones. You are fluent in Python, comfortable with modern ML techniques such as deep learning, sequence models, reinforcement learning, and time-series modeling, and you know what separates a backtest artifact from a durable edge: leakage, overfitting, regime dependence, unrealistic fill assumptions.
• Fluent in Risk and Execution: You understand that realized P&L is a function of costs, capacity, venue economics, and sizing, not just signal quality. You can look at a live result and tell whether the model, the execution, or the market changed.
• Built for Hands-on Ownership: You have led research or technical work and want to stay close to it rather than drift into pure management. You are comfortable navigating considerable ambiguity, and building the process yourself. You want to build AI systems in an environment that tests them against real outcomes, and you can hold near‑term P&L accountability alongside longer‑term research goals.

Nice to Have

• PhD in a quantitative discipline, or equivalent research depth
• Depth in market microstructure and algorithmic execution
• Experience building a research or trading capability inside an early‑stage company

What We Offer

• Competitive Compensation: A competitive base salary with and performance-based bonus.
• Meaningful Ownership: The opportunity to build and own a part of something bigger with meaningful equity ownership.
• Comprehensive Benefits: Medical, dental, and vision coverage designed to support you and your family’s well‑being.
• Financial and Wellness Support: 401(k), Commuter, FSA, and HSA programs, and additional benefits to support long‑term stability.
• Flexible Time Off and Work-Life Balance: Generous vacation, sick leave, and company holidays, with flexibility to recharge when needed.

Additional Information

• This role requires current authorization to work in the United States; Protogon Research is not able to provide visa sponsorship at this time.
• This role is primarily in-person at our Carmel Valley (San Diego, CA) office.