Agentic AI
AI agents that can plan, use tools, and know when to hand things back to a person. I care as much about how we test and watch over them as about how clever they are, because trust is what gets an agent used.
Executive Director · Applied AI/ML · Agentic & Generative AI · NLP · PhD, Biophysics
I lead teams that build AI agents and generative AI tools people actually want to use. I started out as a scientist, and I still love the moment an idea turns into something that works.
I'm an Executive Director of Applied AI/ML at J.P. Morgan Chase in New York. I joined the Machine Learning Center of Excellence as its first hire, and it has been a joy to watch it grow into an AI organization of more than 200 people.
These days my teams build AI agents, generative AI, and language tools that take the busywork out of people's days. I spend a lot of time with the people who use what we build, because the best ideas usually come from watching how they really work. I've also learned how to show clearly what difference the AI made, which is one of the harder and more rewarding parts of the job.
Before AI, I was a scientist. I earned my PhD in biophysics at Johns Hopkins studying how proteins fold, and that curiosity has followed me ever since.
AI agents that can plan, use tools, and know when to hand things back to a person. I care as much about how we test and watch over them as about how clever they are, because trust is what gets an agent used.
Retrieval-augmented generation that drafts well-structured documents from trusted sources. The patent has the details.
Language models that help people spend less time on routine steps and more on the conversations that matter. See the patents on improving sales processes and AI-assisted pricing.
Feedback loops, monitoring, and LLM-as-judge evaluation that keep models improving after launch, plus synthetic data for training them.
Hiring, mentoring, and growing people across machine learning, engineering, and product. Watching someone on the team take on something they didn't think they could do is my favorite part of the job.
The full story is on LinkedIn.
My PhD at Johns Hopkins asked how proteins fold, one repeat at a time. Along the way I grew protein crystals, solved the structure of a new kind of repeat protein, and built models to understand what holds it together.
The structure is a kinesin-light-chain-like protein from the fungus Podospora anserina. Its repeats are nearly identical in sequence, which makes it an unusually clean model system for asking how neighboring repeats stabilize one another.
Each repeat is treated as folded or unfolded, with its own stability plus a coupling energy to its neighbors. Fitting many constructs globally with shared parameters separates the two terms.
The fitting programs, with bootstrapped confidence intervals, were published for the community in Protein Science (2021).
Crystallography, analytical ultracentrifugation, circular dichroism, fluorescence kinetics, and Monte Carlo error analysis.
Structure images: RCSB PDB (rcsb.org), entry 4Y6W; Marold JD, Kavran JM, Bowman GD, Barrick D. Structure 23:2055 (2015).
I grew up on story time and board games, and both stuck.
I love board games and strategy games of all kinds, and I've always been curious about the math behind them: what each move tells you, and how to find the best one.
I enjoy learning other languages. Right now I'm working on Dutch and French.
I love rollerblading and running, and I play beach volleyball whenever the weather allows.
A few games you can play in the browser, with notes on the math behind them. Mastermind is up first.
I'm always glad to hear from people about AI, speaking, research, or mentoring. Email is the best way to reach me.
contact [at] jakemarold [dot] com
I read everything, but I can't respond to unsolicited recruiting or sales outreach.