Steve Lee, Machine Learning Developer in San Jose, CA, United States
Steve Lee

Machine Learning Developer in San Jose, CA, United States

Member since May 7, 2019
Steve is an experienced software engineer with a special interest in machine learning, deep learning, big data, and data science. He has experience in machine learning algorithm development with CNN, RNN, NLP, NLU, decision trees and computer vision, reinforcement learning, and big data processing such as Spark, Hadoop, Hive, and BigQuery. He's also proficient in the deployment of automated machine learning models for production.
Steve is now available for hire


    Scikit-learn, PyTorch, TensorFlow, Python, Data Science, Machine Learning
  • Calix
    Network Protocols, Wireless, Data Science, Machine Learning



San Jose, CA, United States



Preferred Environment

Natural Language Processing (NLP), Computer Vision, C++, Python, NLU, Machine Learning, Big Data, Scikit-learn

The most amazing...

...thing I have productized are several ML models that improve eCommerce sales revenues significantly.


  • Machine Learning Engineer/Principal Software Engineer

    2016 - PRESENT
    • Developed predictive data analytics, visual searches, object detections and classification with machine learning, and deep learning models with large datasets.
    • Developed learning to rank machine learning models to improve online search relevance algorithms for an eCommerce search platform.
    • Developed customer services chatbots and recommender systems with NLU and NLP using machine learning techniques.
    • Analyzed large data sets to develop custom machine learning models and algorithms to drive business solutions.
    • Built large datasets from multiple sources in order to build algorithms for predicting future data characteristics such as time series machine learning models and anomaly detection.
    Technologies: Scikit-learn, PyTorch, TensorFlow, Python, Data Science, Machine Learning
  • Principal Software Engineer

    2015 - 2018
    • Developed a self-learning and self-healing enterprise Cloud WIFI system with a machine learning algorithm.
    • Built data pipeline to retrieve real-time key metrics from IoT devices to manage wireless network performance.
    • Built a predictive data analytics to predict the best channel and coverage to improve network performance.
    Technologies: Network Protocols, Wireless, Data Science, Machine Learning


  • Time Series Forecasting with LSTM (Development)

    Time Series forecasting is an important area in machine learning. This model predicts future web server usages especially for HTTP requests per second. It uses LSTM model, which is a type of recurrent neural network (RNN) that allows the network to retain long-term dependencies at a given time from many time steps before. RNNs were designed to that effect using a simple feedback approach for neurons where the output sequence of data serves as one of the inputs. However, long term dependencies can make the network untrainable due to the vanishing gradient problem. LSTM is designed precisely to solve that problem.

  • Deep Reinforcement Learning Model to Learn to Play an Atari Game (Development)

    This model learns to play an Atari game using deep Q-learning.

    Deep Q-learning uses a deep neural network to maximize the rewards in a given environment. Q-learning is a model-free reinforcement learning algorithm. The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.


  • Languages

    Python, C++, C, SQL, Java
  • Libraries/APIs

    TensorFlow, PyTorch, NumPy, Scikit-learn
  • Paradigms

    Data Science, Anomaly Detection
  • Other

    Machine Learning, Data Analytics, Deep Reinforcement Learning, Deep Learning, Natural Language Processing (NLP), NLU, Chatbots, Computer Vision, Big Data, Wireless, Network Protocols
  • Frameworks



  • Bachelor's degree in Computer Science
    1997 - 2001
    UC Berkeley - Berkeley, CA

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