New York · Applied AI/ML

Jake Marold

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.

Black-and-white portrait of Jake Marold in sunglasses and a leather jacket against a stone wall
01 / About

Current work

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.

200+people in the AI organization I joined as its first hire
10+years building machine learning and AI
2granted U.S. patents, with more pending
PhDin biophysics from Johns Hopkins
02 / Focus

What I'm working on

/01 · featured

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.

/02

Document generation with RAG

Retrieval-augmented generation that drafts well-structured documents from trusted sources. The patent has the details.

/04

Evaluation & feedback

Feedback loops, monitoring, and LLM-as-judge evaluation that keep models improving after launch, plus synthetic data for training them.

/05

Growing teams

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.

03 / Experience

Path

  1. 2020 – Present

    Executive Director, Applied AI/ML

    J.P. Morgan Chase
  2. 2018 – 2020

    Vice President, Applied AI/ML

    J.P. Morgan Chase
  3. 2015 – 2018

    Data Scientist

    J.P. Morgan Chase
  4. 2015

    PhD, Molecular & Computational Biophysics

    Johns Hopkins University

The full story is on LinkedIn.

Speaking

  • 2019, 2025Columbia University, Data Science Institute
  • 2024, 2025Johns Hopkins University, Experiential Learning & Employer Partnerships
  • 2017Boston University, Mathematical Finance

Patents

Selected publications

04 / Research

Before AI: proteins

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.

Ribbon model of PDB 4Y6W, a single chain of stacked alpha-helical repeats colored blue to red from N- to C-terminus Ribbon model of the 4Y6W assembly, with two chains in orange and green
PDB · 4Y6W

A new class of TPR-like repeat protein

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.

Method
X-ray diffraction
Resolution
1.59 Å
Residues
229
Space group
P 21 21 2
Deposited
Feb 2015
Released
Oct 2015
Rectangular protein crystals under a microscope beside a measurement reticle
Growing the crystals. Protein crystals under the microscope, before they were frozen and shot with X-rays. Getting a protein to pack this regularly is often the hardest step.
Left: electron density mesh around tyrosine 58 and glutamate 30. Right: urea denaturation curves for eight repeat constructs
From structure to energetics. Left: electron density resolving a Glu30–Tyr58 hydrogen bond. Right: chemical denaturation of constructs with different numbers of repeats. Adding repeats shifts unfolding to higher urea, the stabilizing effect a nearest-neighbor Ising model quantifies.
/model

Nearest-neighbor Ising analysis

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.

/software

Open analysis tools

The fitting programs, with bootstrapped confidence intervals, were published for the community in Protein Science (2021).

/methods

Biophysics toolkit

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).

05 / Off the clock

Curious by default

I grew up on story time and board games, and both stuck.

Games & strategy

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.

Vintage black-and-white photo of a mother reading a picture book aloud to her two young sons
Where it started Story time with my mom.
/languages

Dutch & French

I enjoy learning other languages. Right now I'm working on Dutch and French.

A pair of lime-yellow Rollerblade inline skates on a wooden floor
Ready to roll
/on the move

Rollerblading, running & volleyball

I love rollerblading and running, and I play beach volleyball whenever the weather allows.

/play

Games

A few games you can play in the browser, with notes on the math behind them. Mastermind is up first.

06 / Contact

Let's talk

I'm always glad to hear from people about AI, speaking, research, or mentoring. Email is the best way to reach me.

I read everything, but I can't respond to unsolicited recruiting or sales outreach.