About Me

I am an Engineering Manager at Facebook on the Instagram Feed Ranking team. Before Facebook, I worked at AWS on machine learning services such as Amazon SageMaker and Amazon Kendra. I did my Ph.D. at the University of Washington and spent several years commercializing my research through a hardware startup. I hold 9 US patents and published more than 80 peer-reviewed academic papers.

My email is:

Longer version about my journey so far 👇

PhD and OpSIS

Ran Ding received his Ph.D. in EE from the University of Washington (UW), Seattle in 2014. With support from Intel, his Ph.D. research contributed to the creation of an open-access foundry platform OpSISNature article, which made silicon photonics technology accessible to more than 150 research and industry institutions. For example, researchers at CalTech and MIT used chips fabricated at OpSIS for cutting-edge research in 3D LiDAR sensing, quantum computing, and deep learning computation acceleration.

Representative work:


During his PhD, Ran co-founded a fabless (Design-as-a-Service) startup commercializing silicon photonics technology. The startup was acquired in 2014 and later rebrandedOVUM article as Elenion, where he continued to lead a 15-person core technical group and delivered the companies first commercial productIMRA in 2017. Nokia acquired Elenion in March 2020.

In 2017, I left my job as a Director at Elenion and switched career to software, you can read more about it here.
Changing Career
It’s been a few year since I did a hard pivot and changed my career, from hardware to software, from a director role to an individual contributor, from something I worked on for 8 years to starting almost from scratch. Writing this to reflect on this decision and journey so far.


Ran joined AWS AI to work on infinitely scalable machine learning algorithms and platforms in Amazon SageMaker. His main focus was neural variational inference and its applications in Natural Language Processing (NLP)AWS blog, webinar.  Later on, he became a founding member of Amazon Kendra, making state-of-the-art deep learning based semantic search and question answering available to the public through this new service.

Representative work:

Amazon SageMaker – Machine Learning – Amazon Web Services
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality…
Amazon Kendra – Intelligent Search – Amazon Web Services
Amazon Kendra is a highly accurate and easy to use enterprise search service that’s powered by machine learning.


Ran kickstarted Compass’ machine learning effort and built its first machine learning team. The team delivered several machine learning services and features end-to-end (real-time recommenders, search ranking, valuation, CRM). In the process, the team built the necessary machine learning infrastructure (event-driven feature store, training and serving pipeline) and defined machine learning development process.

Bootstrapping a Machine Learning Team
What dost it take to introduce machine learning to a company who has not done it before? What are the technology, organization and people aspects to consider when bootstrapping a machine learning team?


Ran currently supports the Instagram Feed Ranking team.

Ran's main interests in machine learning include natural language processing (NLP) (language modeling, topic models, semantic matching, text classification, etc), deep generative models (VAE, GAN, adversarially regularized autoencoders), and learning methods (weak supervision, transfer learning, meta/few-shot learning, domain adaptation).

Ran currently holds 9 US patents and has authored and co-authored more than 80 peer-reviewed academic papersGoogle Scholar.