Jesse Clark
Director, Discovery Experiences at Target
Virginia, United States
Summary
🤩
Rockstar🎓
Top SchoolJesse Clark is a product and engineering leader with over a decade of experience designing AI-driven search and discovery platforms, now leading Discovery Experiences at Target. He combines an MBA-backed strategic perspective with hands-on ML and backend experience—contributing to open-source projects like marqo where he integrated YOLOv5 and multimodal rerankers to enable GPU-parallel search. Jesse has repeatedly built new teams and cross-functional programs at enterprise scale (Capital One, GE, GameStop), translating user-centered design into measurable product outcomes and clear executive communication. He’s equally at home defining identity and architecture strategies as he is prototyping computer-vision backed demos, and he chairs a local high school CS advisory board, reflecting a commitment to STEM mentorship.
10 years of coding experience
9 years of employment as a software developer
Personal Leadership Strategies, Personal Leadership Strategies at Crotonville AlumniB.S Computer Science Mathematics, B.S Computer Science Mathematics at University of Massachusetts AmherstMaster of Business Administration (MBA) Business Administration and Management General, Master of Business Administration (MBA) Business Administration and Management General at University of North Carolina at Chapel Hill
Github Skills (13)
computer-vision10
pytorch10
machine-learning10
python10
transformers9
vector-search9
docker8
parallel-processing8
opencv8
dockers8
natural-language-processing7
tensorflow5
gpt4
Programming languages (4)
Github contributions (5)
marqo-ai/marqo
Aug 2022 - Jan 2023
Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai
Role in this project:
ML Engineer & Backend Developer
ML Engineer & Backend DeveloperContributions:101 reviews, 56 commits, 88 PRs in 5 months
Contributions summary:Jesse primarily contributed to the integration of YOLOv5 object detection, demonstrating an understanding of computer vision models. They also made significant changes to the Marqo's core infrastructure, including refactoring and adding features to support parallel processing and GPU utilization. Furthermore, they contributed to the implementation of the OWL-ViT reranker, showcasing their experience with multimodal models. They enhanced the demo applications, showcasing their involvement across multiple components.
pythonapproximate-nearest-neighbor-searchhnswsimilarity-searchhuggingface-transformers
jn2clark/jn2clark.github.io
Apr 2021 - Jul 2023
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Contributions:20 pushes in 2 years 2 months
templategithub-pages-templatemmistakesmistakesjekyll
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