Engineering Lead (ML Research Focus)

Protogon Research•San Diego, CA

13d•Onsite

About The Position

Protogon Research is an elite team based in San Diego, CA, backed by top VCs including Leap Global Partners, West Wave Capital, 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 a talented Engineering Lead (ML Research Focus) to help us build impactful technology and drive innovation. This role is central to our technical strategy. As our Engineering Lead, you will work with our CEO and our quantitative research leadership to set the direction for our models, infrastructure, and software systems, and oversee the end-to-end development of production AI systems. You will also guide our ML research and translate promising ideas into models that perform in live environments. Our Head of Quantitative Research owns the overall research and trading roadmap. This role is the machine learning depth underneath it — the person closest to the work, leading day-to-day technical execution, making the calls that turn a roadmap into working models, and raising the level of the engineers around them. We work primarily in person and are looking for candidates based in or willing to relocate to the San Diego area.

Requirements

4–6 years of applied ML / AI experience, including 1–2 years leading technical projects or small teams.
Strong track record of shipping ML systems to production in environments where model performance directly impacts outcomes.
Excel at turning ambiguous problems into clear research questions, testable hypotheses, and executable modeling work.
Personally designed models and experiments, not only deployed or integrated existing models, and prioritize measurable performance over novelty for its own sake.
Hands-on experience with modern ML techniques (e.g., deep learning, sequence models, RL, or time-series modeling).
Fluent in Python and familiar with frameworks such as PyTorch, and have experience working with training pipelines, evaluation systems, and production ML infrastructure.
Expect to spend roughly 80% of your time writing code, conducting research, and building models, and 20% directing technical work and mentoring engineers.
Lead by doing the work at a level others want to match, can carry a project end to end without being managed through it, and want to stay close to the technology rather than drift into process.
Current authorization to work in the United States; Protogon Research is not able to provide visa sponsorship at this time.

Nice To Haves

Experience applying ML in quantitative finance or trading
PhD in ML/AI or similar research experience
Experience as the most senior ML voice on a small team, working directly with founders or research leadership
Experience improving ML platforms, data pipelines, or deployment workflows in production systems
Strong collaboration with Portfolio Managers, traders, or domain experts to translate insights into modeling improvements

Responsibilities

Work closely with our CEO and quantitative research leadership to shape the research agenda, then lead its execution: designing, developing, and deploying the production ML models used in live trading environments.
Formulate hypotheses, design experiments, and evaluate new modeling approaches.
Improve predictive performance, robustness, and adaptability across changing market conditions, with a strong focus on practical implementation and measurable real-world impact.
Lead the day-to-day engineering work across both ML and broader software systems.
Make the technical decisions the team runs on, set the standard through your own work, and mentor engineers so the bar rises across the group.
Partner with company leadership on hiring, onboarding, and team development as we grow.
Drive the adoption of AI tools and workflows that help the engineering team move faster across research, software development, planning, documentation, and collaboration, while maintaining high standards for technical rigor and execution.
Contribute to and improve core ML infrastructure, including data pipelines, training workflows, evaluation tooling, and inference systems.
Ensure models are reliable, observable, and performant in both training and production environments.
Work with leadership to identify high-impact modeling opportunities and translate them into executable ML work.
Operate in an early-stage environment where objectives may be loosely defined, validating approaches through rapid experimentation and integrating models into evolving systems.