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ML Engineer
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Stanford University
Master of Science - MS, Computational and Mathematical Engineering
September 1, 2015 – June 1, 2017
Tsinghua University
Bachelor of Science - BS, Industrial Engineering
August 1, 2011 – July 1, 2015
Mariana Minerals
Founding ML Engineer
January 1, 2025 – Present
Remote
Affirm
Senior Machine Learning Engineer
June 1, 2021 – January 1, 2025
San Francisco Bay Area
Kite.com
Machine Learning Engineer
April 1, 2019 – May 1, 2021
San Francisco Bay Area
Sizmek (formerly Rocket Fuel)
Machine Learning Engineer
July 1, 2017 – March 1, 2019
San Francisco Bay Area
Stanford University
Research Assistant
January 1, 2017 – June 1, 2017
Stanford
Workday
Associate Data Scientist Intern
June 1, 2016 – September 1, 2016
San Francisco Bay Area
CSR Times
Industrial Engineer Intern
July 1, 2014 – September 1, 2014
Zhuzhou, Hunan
Machine Learning | Machine Comprehension on SQuaD with Exploration on Attention Mechanism
January 1, 2017 – March 1, 2017
- Build recurrent neural network with Tensorflow for SQuaD, a question answering dataset of triplets (context paragraph, question, answer span). - Created attention matrix and query-aware representation for encoding and decoding. - Analyzed sequence attention mix model and bi-directional flow model.
Artificial Intelligence | Learning from Hints: AI for Playing Threes
September 1, 2016 – December 1, 2016
• Built MDP model for the mobile game Threes (http://asherv.com/threes/). • Implemented and compared the performances of Expectimax Algorithm, Monte Carlo Tree Search and Q-Learning.
Social Network | Interaction of Friendship and Geographic Movements in Location-Based Social Networks
September 1, 2016 – December 1, 2016
• Built multi-layer graphs for location-based social network. • Designed multiple indexes to measure: the extent to which that users tend to explore different places and the extent to which locations tend to bring people together. • Investigated the relationship between the two sets of indexes and found statistically significant result: Explorers are less likely to go to places that bring strangers together.
Machine Learning | Predict the Cuisine Type of A Recipe Based on Ingredients
March 1, 2016 – June 1, 2016
• Tested different basic machine learning algorithms like SVM, Logistic Regression, LDA, etc. • Constructed a domain-specific dictionary for the ingredients in recipes using GloVe, yielding vector representation for every ingredient. • Using this representation as the first layer of neural network, we improved the accuracy by 10% compared with traditional methods.
Decision Making | Decision Modeling on Tuberculosis in Chinese Populations
April 1, 2015 – Present
• Built a decision model and verify the respective cost-effectiveness for PPD test and advanced test, and proposed the decision suggestion for every state. • Built a decision model for TB therapy, considering complicated situations including drug-assistant, and estimated the expected life years and cost using simulation.
Data Science Research | Exploration and Improvement to Random Sampling Algorithms Based on Markov Chain Monte Carlo
November 1, 2014 – June 1, 2015
• As undergraduate thesis, I studied two multi-dimensional random sampling algorithm: Hit-and-Run (HR) Algorithm and Billiard Walk(BW) Algorithm. • Implemented the two algorithms in multi-dimensional space by Matlab, utilizing a method based on bisection to bound the intersection of line and target region. • Analyzed both algorithms by serial correlation, uniformity and speed of convergence. • Improved performance of Hit-and-Run algorithm by adding extra thickness near boundary. • Designed a paradigm for choosing parameters of Billiard Walk algorithm.
Cultural Fit Analysis
The candidate's project portfolio is diverse, showcasing interests in AI, decision modeling, social networks, and machine learning. Their professional experience spans various companies (Affirm, Kite.com, Sizmek) and roles, indicating adaptability. However, the target role is 'Data Analyst', while the candidate's experience is heavily skewed towards 'Machine Learning Engineer' and 'ML Infra Engineer'. This suggests a potential mismatch in the desired role responsibilities and the candidate's primary expertise, which might impact cultural fit for a pure data analyst role. The candidate's background is more aligned with a senior ML engineer or data scientist position.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong analytical and problem-solving mindset. Experience in diverse projects from AI games to social networks and medical decision-making suggests adaptability and intellectual curiosity. The role as 'Founding ML Engineer' implies leadership and initiative, though details are sparse. The candidate's experience aligns well with roles requiring deep technical expertise and the ability to build complex ML systems.