Senior Machine Learning Engineer
2019 - PRESENTApple- Deployed high-performance deep-learning models to run on-device to improve inference speed and user privacy.
- Led the design of a deep-learning inference stack for on-device and cloud for first and third-party developers at Apple.
- Served as a tech lead and helped grow the team from three to ten engineers and researchers.
Technologies: Tools, Core ML, Git, PyTorch, TensorFlowSenior Director of Engineering
2015 - 2019Petuum- Worked as part of the founding team and helped to grow the engineering team from less than ten in 2016 to over 60 full-time engineers in the second quarter of 2018 (2Q18).
- Influenced—as a domain expert in machine learning, a trench worker and a manager—the product design and delivered quarterly internal platform product milestones from 3Q17-1Q18.
- Led the medical imaging team of four and personally developed best-in-class deep learning methods for chest X-rays to assist radiologists in diagnosis. The project led to the first contract for the company.
- Served as the tech lead and managed the back-end design of Petuum's AI platform that combined all stages of machine learning, including data cleaning, feature engineering, data and model visualization, deployment, and monitoring.
Technologies: Protocol Buffers, Kubernetes, Docker, GraphQL, C++, Spark, TensorFlowSoftware Engineer Intern
2015 - 2015Facebook- Contributed more than 10,000 lines of C++ code during my 3-month internship and helped initiate a large-scale distributed training system for Facebook newsfeed and ad algorithms.
- Developed a distributed large-scale logistic regression using LBFGS solver and parameter server-based synchronization.
- Benchmarked the implementation against Facebook's internal system and Vowpal Wabbit; showed that the implementation achieves high system throughputs and produces comparable to better models.
Technologies: Tools, Facebook, Vowpal Wabbit, C++Software Engineering Intern
2013 - 2013Google- Optimized one of the world’s largest machine learning systems: Google’s ads algorithms for click-through-rate prediction.
- Developed a large-scale hyperparameter tuning framework to enable Google’s ads training system to discover the best hyperparameter settings for ML models based on convex and non-convex optimization methods. The system is used by dozens of teams and later published as Google Vizier.
Technologies: Tools, Google, Protocol Buffers, C++Software Developer Intern
2012 - 2012LinkedIn- Implemented a number of background tasks in the payment backend.
- Improved access control for internal payment handling.
- Developed a dynamic terminal folder navigator (to replace the "CD" in Linux) in a hackathon.
Technologies: Spring, Python, SQL, Java
David Dai
Machine Learning Developer in Seattle, WA, United States
Member since January 29, 2020
David has extensive experience building machine learning and deep learning (DL) capable solutions in top companies (such as Google, Facebook, Apple, LinkedIn), startups (Petuum), and academia (earning a Ph.D. from Carnegie Mellon University, with publications in top venues and 1,700 citations). He holds multiple patents in DL-based medical imaging tech and large-scale AI systems and has grown an AI team to 60+ as the director and tech lead.
David is now available for hire
Experience
- Machine Learning 10 years
- Distributed Systems 8 years
- Deep Learning 6 years
- TensorFlow 4 years
- Natural Language Processing (NLP) 4 years
- Computer Vision 3 years
- PyTorch 2 years
Availability
Part-time
Preferred Environment
C++, Python, Scikit-learn, PyTorch, TensorFlow
The most amazing...
...project was developing best-in-class deep learning methods for chest x-ray to assist radiologists in diagnosis, resulting in the first contract for my company.
Employment
Experience
- Chest X-ray Organ Segmentation Through Adversarial Deep Neural Networks
Motivation:
A medical imaging device client needed to differentiate their products with AI.
Brainstorming:
I worked closely with radiologists to identify tasks that can be automated by AI in the near term. While most AI apps in medical imaging aren’t interpretable (e.g., black box classification of diseases), the solution needed to be interpretable to build trust with doctors. We chose chest X-ray segmentation, which can be visualized and directly leads to clinical indicators.
Development:
Existing methods all use traditional computer vision pipelines and are very brittle and not accurate. I found only a minimal dataset with pixel labels (<300 samples), which was challenging for data-hungry neural networks. After intensive studies, I successfully reduced the ImageNet model by 500x to adapt to a small training dataset and achieved human accuracy for the first time.
Deployment and Impact:
I led a team of four engineers to produce the model for the client, including building a REST API endpoints on public clouds designated by the client for security and latency considerations. I trained the team to fine-tune the model on client data. The developed solution landed the first contract for our company. - Wayfinder Vizier: Rich Question Answering via Neural Semantic Search
Wayfinder Vizier enables rich question answering over any texts, including customer reviews, social media, articles, and FAQs, and brings contextual information retrieval beyond keyword-based search.
For example, Wayfinder Vizier answers the following questions about Chihuly Garden and Glass (a tourist destination) based on TripAdvisor user reviews (out-of-box and without any tuning). The answers (excerpts from user reviews in this case) are both diverse and relevant:
Q: what's the best time to visit the museum?
- A sunny day is the best time to visit
- I think the garden at night is the best time to view it
- Best time to visit is just before the sunset
- My family of three made our reservations for 7:00 pm (they request not only the date of your visit, but you must select a time that you will visit). It was the perfect time to visit.
- Summer is not the best time to visit
Q: Is it a good idea to bring kids?
- Not a place to bring young kids
- It may not be worth it to bring the little ones
- Is a good attraction for families
- What a great place to visit with kids
- Not a place for kids
Wayfinder Vizier was selected as one of 10 Emerging AI Pioneers to present at O'Reilly AI Conference 2019.
Skills
Languages
Python, C++, GraphQL, SQL, JavaOther
Machine Learning, Deep Learning, Distributed Systems, Deep Neural Networks, Computer Vision, Natural Language Understanding (NLU), Natural Language Processing (NLP), Tools, Protocol Buffers, Vowpal Wabbit, Facebook, GoogleLibraries/APIs
TensorFlow, PyTorch, Scikit-learnPlatforms
Linux, Docker, KubernetesFrameworks
Core ML, Spark, SpringTools
GitStorage
MySQL, RocksDB, LevelDB
Education
- Ph.D. Degree in Machine Learning2012 - 2018Carnegie Mellon University - Pittsburgh, PA, USA
- Bachelor of Science Degree in Computer Science2010 - 2012Caltech | California Institute of Technology - Pasadena, CA, USA
- Bachelor of Arts Degree in Physics and Mathematics2007 - 2010Wesleyan University - Middletown, CT, USA
