Engineering Lead (ML Research Focus)

Protogon Research•San Diego, CA

8h•Onsite

About The Position

Protogon Research is a small, VC-backed team based in San Diego, CA, 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: you will lead the entire engineering team, 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. We work primarily in person and are looking for candidates based in or willing to relocate to the San Diego area.

Requirements

5–8 years of applied ML / AI experience
3+ years of engineering leadership experience, including managing full engineering teams (not just AI/ML)
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.
Proven track record of leading engineering teams, not just AI/ML pods, but broader eng organizations.
Expect to spend roughly 70% of your time writing code, conducting research, and building models, and 30% on management and leadership.
Can set and execute on engineering-wide strategy, remain deeply involved in day-to-day technical work, and build a high-performing team culture around technical excellence.
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 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

Own the research, design, development, and deployment of production ML models used in live trading environments.
Formulate hypotheses, design experiments, and evaluate new modeling approaches.
Work closely with leadership and engineering partners to improve predictive performance, robustness, and adaptability across changing market conditions, with a strong focus on practical implementation and measurable real-world impact.
Lead the entire engineering organization, overseeing both ML engineers and broader software engineers.
Set technical direction across the team, combine hands-on AI/ML execution with engineering management, and own hiring, onboarding, and development of engineers in partnership with company leadership.
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 directly 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.