Meghanath Macha, Ph.D.

CTO

San Jose, California, United States11 yrs 8 mos experience

Highly StableAI Enabled

Key Highlights

Led AI strategy for Fortune 500 brands.
Over 15 patents and 10+ publications.
Expert in bridging AI and marketing intelligence.

Stackforce AI infers this person is a MarTech expert specializing in AI-driven marketing solutions.

Contact

Skills

Core Skills

Ai StrategyDeep LearningMachine LearningResearchData Science

Other Skills

AlgorithmsCC++Computer VisionHTMLJavaLinuxMatlabMicrosoft ExcelMicrosoft OfficeMySQLNatural Language Processing (NLP)Operating SystemsPHPPowerPoint

About

I lead AI strategy and platform development at ZeroToOne.AI, where we build foundational behavioral models that process billions of data points daily. Our mission is to transform personalized marketing by delivering predictive intelligence at massive scale for Fortune 500 brands. What started as a stealth startup, where I was a Founding Engineer, has evolved into a platform powering deep behavioral insights and real-time audience predictions. Before ZeroToOne.AI, I worked as a Senior Machine Learning Engineer at Adobe, driving GenAI innovations for brand safe enterprise content creation. I played a key role in launching Adobe GenStudio Create and Adobe’s first AI-powered marketing campaign, alongside building scalable deep learning models for Adobe Firefly’s content tagging services. My Ph.D. research at Carnegie Mellon University focused on modeling consumer behavior through large-scale location data. That research laid the groundwork for ZeroToOne.AI’s core technology, which today supports multiple Fortune 500 clients and drives substantial business growth. With over 15 patents and 10+ publications in top venues like KDD, Management Science, ISR, and WebConf, I’m passionate about bridging complex data with actionable marketing intelligence. I thrive on solving hard problems at the intersection of AI, behavioral science, and real-world impact.

Experience

Zerotoone.ai

Head of Artificial Intelligence

May 2025 – Present · 10 mos · Remote

Lead the development of scalable AI platform focused on foundational behavioral models that process billions of data points daily to predict consumer intent and segment audiences for Fortune 500 clients. Architect systems combining large-scale behavioral data with deep learning models to deliver real-time, personalized marketing insights.
Key responsibilities include:
1. Designing and implementing high-throughput data pipelines and feature engineering workflows.
2. Developing transformer-based models and embedding techniques to capture complex user behavior patterns.
3. Building multi-task learning frameworks to improve prediction accuracy across diverse behaviors.
4. Ensuring continuous model retraining and deployment to adapt to evolving data signals.
5. Collaborating closely with cross-functional teams to translate AI outputs into actionable marketing strategies.

Predictive ModelingTensorFlowDeep LearningNatural Language Processing (NLP)AlgorithmsAI Strategy

Adobe

3 roles

Senior Machine Learning Engineer

Jan 2022 – May 2025 · 3 yrs 4 mos · San Jose, California, United States

Deep LearningNatural Language Processing (NLP)Predictive ModelingTensorFlowComputer VisionMachine Learning

Machine Learning Engineer

May 2021 – Jan 2022 · 8 mos · San Jose, California, United States

Deep LearningNatural Language Processing (NLP)Predictive ModelingTensorFlowComputer VisionMachine Learning

Data Scientist

May 2019 – Aug 2019 · 3 mos · San Francisco Bay Area

Worked on semi-supervised identification and summarization of marketing segments and critical event detection in online web sessions. Two patents filed, workshop paper at WebConf 2020 published.

Deep LearningData Science

Amazon

Applied Scientist

May 2018 – Aug 2018 · 3 mos · Greater Seattle Area

Worked on developing active learning techniques for the record linkage problem.

Deep LearningResearch

Adobe

4 roles

Data Scientist

May 2017 – Aug 2017 · 3 mos · San Francisco Bay Area

Worked on click-fraud detection which involved identifying fraudulent publishers and users. Paper published in ECML PKDD 2018.

Member of Research Staff - 2

Dec 2014 – Jul 2016 · 1 yr 7 mos · Bangalore, India

Member of Research Staff

Jun 2014 – Dec 2014 · 6 mos · Bangalore, India

Research Intern - Advanced Technology Lab.

Apr 2013 – Jul 2013 · 3 mos · Bangalore , India.

During the three months I spent at Adobe, me and a group of two other interns mentored by Mr. Deepak Pai, Research Scientist, Adobe ATL worked on analysing Click streams.We built two models which could classify the visitor sessions on a web site based on the URLs visited in a session.

Carnegie mellon university

Graduate Research Assistant

Jul 2016 – Mar 2021 · 4 yrs 8 mos · Greater Pittsburgh Area

Deep LearningResearch

Ubisoft

Technical Intern

May 2012 – Jul 2012 · 2 mos · Montreal, Canada Area

In the summer of 2012, I had the chance to work under the guidance of Professor Yoshua Bengio one of the prominent researchers in the field of Deep learning at Université de Montréal. I worked in the Ubisoft - Université de Montréal industrial chair, building a model which would predict a list of gaming inventory a user is most likely to purchase based on his prior transactions and gaming profile.

Chitika, inc.

Software Internship

May 2011 – Jul 2011 · 2 mos · Hyderabad Area, India

I had the opportunity to do my internship at the end of my sophomore year at Chitika, an Online Advertising Network where I had to build a model to classify a given web page into predefined categories which could be used for targeted advertising.I modeled a Naive Bayes classifier for this purpose incorporating Co-training, a semi supervised learning technique which considered text on the page as one view and the anchor text of hyperlinks on the page as the other view.