# Hao Li

> Staff Engineer at Google

Location: Mountain View, California, United States
Profile: https://flows.cv/haoli

Not open to new opportunities. My LinkedIn activities reflect my personal opinion.

## Work Experience
### Staff Software Engineer @ YouTube
Jan 2023 – Present | San Francisco Bay Area
Give everyone a voice and show them the world through short-form videos!

YouTube Shorts Discovery, tech lead (~80 launches in the areas below)
* a new exciting initiative that blurs the boundary between consumption and creation
* re-ranking
* "query-level" intent modeling and responsive feed
* multi-modal large model (Gemini) x shorts recommendation

Community contributions
* YouTube privacy delegate
* gMentor
* TL excellence program

20+ interviews on machine learning engineering, and data structure / algorithms

### Staff Machine Learning Engineer @ Twitter
Jan 2021 – Jan 2023 | Greater Seattle Area
Ads Product ML leadership - group tech lead supporting retrieval, targeting/audience, user and content understanding, and identity/attribution/measurement. 

We measure and optimize through rapid and rigorous experimentations offline and online.
* re-architect the ads serving early funnel with additional retrieval components
* ad attribution science problem space scoping and roadmapping (model-based id bridging and conversion matching)
* audience expansion rebuild

Experimentation Shepherd and Mentor.
Ads Product ML mentor.

### Applied Scientist @ Amazon
Jan 2018 – Jan 2021 | Greater Seattle Area
design and experiment ads ranking and allocation mechanisms for long-term customer success:
* long-term aware ads allocation
* long-exposure long-term impact holdout experiment design
* strategically-important segments contextual optimization
* opportunity cost predictions and applications
* scalable ads relevance management
* offline simulation (replay)
* causal impact of ads configuration


Weblab bar-raiser, who oversees and approves launches across the ads org. 

4 peer-reviewed papers published at amazon machine learning conference over my 3 years. 


Misc.
Instructor at Amazon Machine Learning University

Speaker at Auction Summit (Seattle, 2018), ML Workshop (Bangalore, 2018), AdTech Meetup (Palo Alto, 2019), Amazon Machine Learning Conference (Seattle, 2019 and 2020)

Reviewer for internal research proposals/papers in the tracks of "Advertising", "Personalization and Recommendations", "Search and Information Retrieval", and "Data Science".

120+ interviews on machine learning, and data structure / algorithms.

### Data Scientist @ Apple
Jan 2016 – Jan 2018 | San Francisco Bay Area
Cool stuff - still not sure if I can disclose.

### Data Scientist @ Microsoft
Jan 2015 – Jan 2016 | Greater Seattle Area
Windows Core Data Science


## Education
### Master of Science (M.S.) in Operations Research
Columbia University

### Bachelor of Science (B.S.) in Mathematics and Applied Mathematics
Zhejiang University


## Contact & Social
- LinkedIn: https://linkedin.com/in/hl2777

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Source: https://flows.cv/haoli
JSON Resume: https://flows.cv/haoli/resume.json
Last updated: 2026-04-12