# Haojun L.

> AI Engineer@LinkedIn | MSCS@Stanford

Location: San Francisco Bay Area, United States
Profile: https://flows.cv/haojunl

As an AI Software Engineer at LinkedIn with years of industry experience, I focus on advancing natural language processing and machine learning systems to enhance user experiences. Leveraging expertise in large language models (LLMs), I contribute to cutting-edge innovations that support LinkedIn’s mission to connect professionals worldwide. My work emphasizes the design, deployment, and optimization of scalable AI solutions, ensuring performance and reliability.

## Work Experience
### AI Software Engineer @ LinkedIn
Jan 2024 – Present | Mountain View, CA
• Implemented embedding-based retrieval with the Qwen model. Identified query understanding as a key component in retrieval through evidence based approaches.  
• Productionized a profile summarization model at scale, summarizing millions of profiles to improve ranking efficiency.
• Built an incremental summarization pipeline, reducing model inferencing duplication and saving resources. Enhanced workflow efficiency by 10x, demonstrating a strong impact on operational effectiveness.

### Senior Machine Learning Engineer (NLP/NLU) @ Moveworks
Jan 2021 – Jan 2024 | Mountain View, CA
- Designed and implemented a brand new LLM-based people disambiguation and lookup product. Worked cross-functionally with Infra, ML, and Annotation teams.
- Built a LLM-based answer summarization module for the Answers product.
– Reproduced, evaluated, and deployed Transformer based slot filling model. Owning the full lifecycle of the model.
– Designed, implemented, and deployed a new Multi-annotator Search Relevance annotation interface. Designing and implementing both the front end (REACT) and backend (Django, Postgres). Worked closely with the annotation team to develop the interface. Surfaced valuable agreement statistics and greatly improved data quality. Implemented Dashboards to visualize data quality and inter-annotator agreement.
– Designed a new Entity Linking annotation interface to collect EL data. Worked closely with in-house annotators, UI/UX team, and potential downstream consumers of such signal.
– Re-designed the internal Knowledge Graph representation and developed a new ontology with annotators and downstream consumers.
– Designed and implemented multiple Entity management UI features.

### Research Assistant @ Stanford University
Jan 2019 – Jan 2021 | San Francisco Bay Area
– Competing in the Alexa Prize Grand Challenge building a social chat bot.
– Researching on Entity Linking and Offensive Users in Open-domain Dialogue
– Advised by Prof. Chris D. Manning

### Course Assistant @ Stanford University
Jan 2018 – Jan 2021 | San Francisco Bay Area
- Head CA for CS234 (Reinforcement Learning)
- CA for CS229 (Machine Learning), CS 161 (Algorithms), CS224U (NLU)
– Build and Develop course materials including discussion handouts, home-work, and exam materials
– Lectured discussion section explaining core concepts in algorithm design and analysis

### Core ML Infrastructure Engineer Intern @ Hive
Jan 2019 – Jan 2019 | San Francisco Bay Area
– Designed, implemented, tested, and deployed a model management service. Enabled automatic model management on Tensorflow Serving
– Designed, implemented, tested, and deployed a request prioritization gateway for Tensorflow Serving

### Software Engineer @ AppDynamics
Jan 2017 – Jan 2018 | San Francisco Bay Area
– Inventor of mid method bytecode injection in Java. Allowed code instrumentation at arbitrary locations at runtime. (Patent 10223237)
– Upgraded Java Agent logging architecture. Improved performance by more than 10% through async logging.
– Designed and Implemented runtime field injection, reducing overhead in highly async customer environments by 30%
– Designed and Implemented an extension to the Agent iSDK, enabling customer and support teams to create custom async instrumentation points
– Migrated product from Gerrit + Jenkins to BitBucket + Teamcity in one week.
– Decoupled highly complex code using Dagger IoC framework.

### Software Engineer Intern @ AppDynamics
Jan 2016 – Jan 2016 | San Francisco Bay Area
•	Led a project of implementing mid method bytecode injection in Java. Successfully integrated the project with existing code and laid foundation of mid method instrumentation.
•	Led a hackathon project implementing a Slack bot to address common intern questions. Automating timesheet process and journal logging. Implemented Markov chain and set up MongoDB databases.
•	Created solutions to reduce memory and storage usage. Solving numerous bugs

### Software Engineer Intern @ IBM
Jan 2015 – Jan 2015 | San Jose, California
1. Initiate and Lead HAL (Heuristicly programmed Algorithm) project, building an AI to control z/OS and its subsystems.
2. Team lead and programmer for front-end and server side functionality. Used RESTful APIs and WebSocket APIs.
3. Coordinate teammates on front-end and back-end integration and code reviewing.


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

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Source: https://flows.cv/haojunl
JSON Resume: https://flows.cv/haojunl/resume.json
Last updated: 2026-03-29