Steve is available for hire

Steve Lee

Verified Expert in Engineering

Machine Learning Developer

Location

San Jose, CA, United States

Toptal Member Since

November 27, 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.

Machine Learning Data Analytics Deep Learning Natural Language Processing (NLP)NLU C Python C++TensorFlow PyTorch NumPy Big Data Computer Vision SQL Java

Portfolio

CODA AI

Scikit-learn, PyTorch, TensorFlow, Python, Data Science, Machine Learning

Calix

Network Protocols, Wireless, Data Science, Machine Learning

Experience

C++ - 10 years Python - 10 years Machine Learning - 8 years PyTorch - 5 years Big Data - 5 years GPT - 5 years Natural Language Processing (NLP) - 5 years Generative Pre-trained Transformers (GPT) - 5 years

Availability

Full-time

Preferred Environment

GPT, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), 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.

Work Experience

Machine Learning Engineer/Principal Software Engineer

2016 - PRESENT

CODA AI

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

Calix

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

Experience

Time Series Forecasting with LSTM

https://github.com/stevieBot/time_series_forecasting

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

https://github.com/stevieBot/dqnAtari

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.

Skills

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, GPT, Generative Pre-trained Transformers (GPT), Computer Vision, Big Data, Wireless, Network Protocols, Computer Science

Frameworks

Spark

Education

2010 - 2014

Bachelor's Degree in Computer Science

UC Berkeley - Berkeley, CA

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