Engineering Lead; ML Research Focus

Engineering Lead (ML Research Focus)

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 True Fi 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.

This role is expected to be primarily hands-on, with the majority of time spent researching, building, training, and deploying models, while leading day-to-day technical execution across the engineering team.

• ML Research & Model Development:
  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.
• Technical Leadership & Mentorship:
  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.
• AI-Enabled Engineering Acceleration:
  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.
• AI Systems, Data Pipelines & Model Quality:
  Contribute to and improve core ML infrastructure, including data pipelines, training workflows, evaluation tooling, and inference systems. Much of this is still being built rather than maintained, and you will have real latitude over how it takes shape. Ensure models are reliable, observable, and performant in both training and production environments.
• Execution Against Trading Objectives:
  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.