About the job Head of Quantitative Research
Responsibilities and ExpectationsYour 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.
- 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. - 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.- 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. - 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.
- 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.
