# Chintan Shah > Staff Machine Learning Engineer, ML R&D at PathAI Location: Greater Boston, United States Profile: https://flows.cv/chintanshah Passionate machine learning leader driven by a curiosity for tackling critical, high-impact problems. I thrive on discovering hidden patterns in complex systems and translating them into robust, scalable solutions that make a real-world difference. I’ve often been described as an "independent self-starter, a relentless learner, and above all, a good human being." Currently, I’m a Senior ML Engineer at PathAI, where I’ve spearheaded multi-resolution modeling, robust pan-domain classifiers, and foundation models that have become the backbone of our platform while dramatically reducing costs and powering breakthroughs across diagnostics and drug development. Earlier, as a graduate researcher under Dr. Rose Yu, I pioneered graph neural network approaches for tracing epidemic sources, establishing fundamental theoretical limits and practical tools — work that earned me Northeastern’s Graduate Research Award (placing me in the top 0.3% of peers). Before that, I built and led production-scale machine learning and optimization systems at Media.net, where our innovations drove substantial revenue gains and set architectural standards still in use today. I’m passionate about pushing the frontiers of machine learning — blending rigorous research with an entrepreneurial mindset to build systems that solve some of the toughest challenges, across domains. Outside of work, you’ll likely find me hopping between coffee shops, deep into a Kindle read, or on the tennis court. ## Work Experience ### Staff Machine Learning Engineer @ PathAI Jan 2025 – Present Foundation Models, Multi-Modal Models, Agents ### Senior Machine Learning Engineer @ PathAI Jan 2023 – Jan 2025 | Boston, Massachusetts, United States Foundation Models to Vision Language Models ### Machine Learning Engineer lll @ PathAI Jan 2023 – Jan 2023 | Boston, Massachusetts, United States - Developed novel Multi-Resolution classifiers to model the pyramid structure of histopathology slides with better fidelity ### Machine Learning Engineer II @ PathAI Jan 2022 – Jan 2023 | Boston, Massachusetts, United States - Analyzed and developed augmentation policies to improve model robustness and generalization ### Machine Learning Engineer @ PathAI Jan 2021 – Jan 2022 | Boston, Massachusetts, United States - Researched and implemented analytical frameworks to evaluate robustness of clinical models - Researched modeling techniques to segment tissue regions for understanding Biliary Tract Carcinoma growth patterns ### Machine Learning Research Assistant, Spatiotemporal Learning @ Khoury College of Computer Sciences Jan 2019 – Jan 2021 | Boston, Massachusetts - Awarded Khoury Graduate Research Award, 2021 for this work Research area: Learning physical dynamical processes using Graph Neural Networks (GNN) - Won funding to study the effectiveness of GNNs in locating the source of an epidemic over a network - Designed graph neural network (GNN) architectures that can identify P0 close to the theoretical limit on accuracy. - Evaluated effectiveness off GNN in terms of speed and accuracy and found them to be over 100x faster and 20% more accurate compared to current state-of-the-art methods all the while remaining epidemic-“model-free”. - Led the project and conducted weekly meetings. Pre-print available here: https://arxiv.org/abs/2006.11913 ### Machine Learning Intern @ PathAI Jan 2020 – Jan 2020 | Boston, Massachusetts, United States PathAI's mission is to improve patient outcomes with AI-powered pathology. Our platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine learning. - Designed models that generalize out-of-training distribution for histopathology slides ### Machine Learning Intern @ Apprentice Health Jan 2019 – Jan 2019 | Boston - Designed a permutation-invariant machine learning architecture to predict in-clinic wait time (in real-time) to improve in-clinic patient satisfaction rates. - Designed a discrete optimization framework using evolutionary algorithms to streamline provider schedules that reduce patient-wait time by over 40%. - Architected high-throughput machine learning pipelines in Python using Ray and Kubernetes on AWS. About: Apprentice Heath is a <10-person YC-backed startup aiming to revolutionize scheduling and workflow processes in the healthcare industry using machine learning approaches. In this internship, I researched and designed machine learning systems and gradient-free optimization techniques to solve problems in healthcare operations. ### Graduate Teaching Assistant @ Khoury College of Computer Sciences Jan 2019 – Jan 2019 | Greater Boston Area Teaching Assistant for CS 5010: Program Design Paradigms - Responsible for code reviews, grading, and conducting labs and office hours to give feedback on code design. ### Senior Software Engineer @ Directi Jan 2017 – Jan 2018 Summary: Product owner and team-lead in the core revenue optimization team for Media.Net Contextual Advertising (Directi). In this team, I led several technical and research initiatives to understand major business pain-points. I mentored and managed a team of four software engineers on developing revenue optimization, data streamlining, and analytics platforms. - Developed a micro-services based bid optimization system using time-series forecasting techniques to increase daily profit by 22% - Designed sentiment analysis, text summarization, and keyword ranking systems for streamlining business intelligence ### Software Engineer @ Directi Jan 2015 – Jan 2017 - Designed Keyword intelligence and Ad Text generation algorithms using linguistic techniques to reduce campaign management time by 70% - Streamlined data architectures using stream processing topologies to reduce data ingestion time from 45 minutes to 2.5 minutes ### Research Assistant @ Indian Institute of Technology, Bombay Jan 2017 – Jan 2017 - Contributed an open-source machine-consumable VerbNet model to Natural Language Toolkit (NLTK) in Python - Extended the existing machine translation system by infusing Panini-inspired karaka features (Hindi) with VerbNet inspired frames (English) to construct linguistically complete Context-Free Grammars. - Explored Bi-Directional LSTM models for the task of machine translation - Implemented a caching system in Redis to reduce average response times for repetitive translation tasks. - Coordinated the project and led the initiative in a team of two PhD students and one MSc student About: I worked in the lab of Dr. Ganesh Ramakrishnan on the task of Statistical Relational Learning for Machine Translation for low-resource languages. ### Software Development Intern @ Mobiuso Jan 2014 – Jan 2015 ### Co-founder @ Lazywyre.com Jan 2012 – Jan 2015 • Engineered and implemented the initial technology stack and other facets of the technology product including product roadmap, product feature decisions and user experience. • Setup the early supply-chain and logistics system and overlooked the daily operations. • Built and managed cross-functional teams to a total team size of 7 including 4 full-time hires and 3 interns. • Developed and open-sourced an android application for Order and Inventory management in PrestaShop built using Ionic framework and AngularJS. ## Education ### Master's degree in Computer Science Khoury College of Computer Sciences ### Bachelor of Engineering (B.E.) with Distinction University of Mumbai ### High School Dr. Sarvepalli Radhakrishnan Vidyalaya ## Contact & Social - LinkedIn: https://linkedin.com/in/chnsh - Portfolio: https://chnsh.me/ --- Source: https://flows.cv/chintanshah JSON Resume: https://flows.cv/chintanshah/resume.json Last updated: 2026-03-31