Jianjie Liu
Senior Machine Learning Engineer at AKASA
Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolJianjie Liu is a Senior Machine Learning Engineer with 10 years of experience building production-grade ML systems, currently focused on scaling and deploying large language models. At AKASA he extended in-house LLM context windows from 32k to 180k tokens, boosted inference throughput up to 6.7x, and created a standardized model artifact interface that cut iteration cycles from months to days. His background at Microsoft spans MLOps for recommender systems, AI acceleration projects, and game-focused RL infrastructure, and he contributes to popular open-source recommender tooling. Practical and curious, he pairs research-first attention to quantization and attention optimizations with hands-on engineering that reduces operational cost and API spend. Outside of engineering he’s an amateur street and portrait photographer, reflecting a designer’s eye that informs his data and model intuitions.
9 years of coding experience
5 years of employment as a software developer
Self-Driving Car Engineer Nanodegree Autonomous Vehicle, Self-Driving Car Engineer Nanodegree Autonomous Vehicle at UdacityBachelor's degree Computer Science, Bachelor's degree Computer Science at Tufts UniversityBoston Latin Academy
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (11)
machine-learning10
pyspark10
recommendation-system10
data-pipelines10
data-pipeline10
python10
data-science10
data-analysis9
testing9
jupyter-notebook9
tensorflow6
Programming languages (5)
Github contributions (5)
recommenders-team/recommenders
Aug 2021 - Mar 2022
Best Practices on Recommendation Systems
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:62 reviews, 153 commits, 14 PRs in 7 months
Contributions summary:Jianjie contributed to the project by implementing configuration management using `tox` and added test markers, indicating efforts to improve the development workflow. They also worked on a mock schema experiment, suggesting involvement in data modeling or schema design. The user further modified an example notebook focused on diversity metrics, implying a focus on evaluating recommendation algorithms. They modified the dataset and evaluation code using pyspark, which included changes to data types and the use of CSV.
recommendation-systemspythonjupyter-notebookoperationalizationmicrosoft
laserprec/AutoDrive
Nov 2017 - Apr 2019
Contributions:37 commits, 35 pushes, 3 branches in 1 year 4 months
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