Clayton Mellina
Chief Technology Officer at Transcripta Bio
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolClayton Mellina is a seasoned technology leader and Chief Technology Officer at Transcripta Bio with 14 years of experience applying computer vision, deep learning, and scalable engineering to real-world products. He moved from leading applied vision R&D and product launches at Google Cloud—where he led teams that shipped Vertex AI Vision and Visual Inspection AI—to building omics-first ML platforms for drug discovery and high-throughput biology. Clayton combines hands-on research (open-source contributions to domain-adaptive networks like MNIST-DANN) with product delivery at scale, having architected billion-image similarity search systems at Flickr. He is comfortable bridging customers, research, and engineering, running pilot deployments with enterprise partners and turning prototypes into revenue-driving services. Trained at Stanford in HCI and AI, he brings a rare mix of academic rigor, startup scrappiness (early employee at LookFlow), and enterprise execution in the Bay Area. An interesting through-line: he consistently focuses on making ML systems robust in noisy, real-world domains—whether images or biological data.
14 years of coding experience
11 years of employment as a software developer
Master of Science Computer Science Dual focus in Human-Computer Interaction and Artificial Intelligence, Master of Science Computer Science Dual focus in Human-Computer Interaction and Artificial Intelligence at Stanford University
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
tensorflow10
adaptation10
python10
machine-learning9
data-visualisation5
data-visualization5
data-visualizations5
Programming languages (7)
Github contributions (5)
pumpikano/tf-dann
Jun 2016 - Dec 2021
Domain-Adversarial Neural Network in Tensorflow
Role in this project:
ML Engineer
ML EngineerContributions:21 commits, 4 PRs, 14 pushes in 5 years 6 months
Contributions summary:Clayton contributed to the MNIST-DANN experiment by adding and updating the implementation for the MNIST-M dataset, indicating an interest in domain adaptation tasks. They also added a simpler blobs example, demonstrating an ability to work with different datasets and model behaviors. Furthermore, they added a flip_gradient implementation to the project. Their changes suggest a focus on enhancing and experimenting with the core domain adaptation model within the repository.
adversarial-learningdeep-learningadversarialtensorflow-modelsneural-network
yahoo/lopq
Dec 2015 - Feb 2017
Training of Locally Optimized Product Quantization (LOPQ) models for approximate nearest neighbor search of high dimensional data in Python and Spark.
Contributions:38 commits, 15 PRs, 16 pushes in 1 year 1 month
high-dimensional-datapythonapproximate-nearest-neighbor-searchneighbor-searchtraining
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