Software Engineer | Machine Learning2022 - PRESENT
Technologies: Artificial Intelligence (AI), Python, C++, SQL, Hyperparameters
- Performed relevant work in a horizontal machine-learning team.
- Collaborated with various stakeholders in different organizations from my own.
- Built software which resulted essential to the company.
Senior Machine Learning Engineer2020 - PRESENTKUNGFU.AI Advanced Data Science Services
Technologies: Google Cloud Platform (GCP), TensorFlow, PyTorch, Python 3
- Characterized behavior of an unsupervised learning model meant for government use and implemented code and tests for its deployment.
- Developed project plan and tracked using Asana with agile principles.
- Delivered regular status updates to clients and facilitated requirements discussions.
Deep Learning Engineer (Hybrid Data Scientist and Data Engineer)2018 - 2020Node.io
Technologies: PyTorch, Linux, Amazon Web Services (AWS), Amazon, Python
- Gave Node.io its first real AI capabilities--built a predictive modeling and analytics stack from the ground up with scalable AWS (ECS, EC2, Docker) and ML (feature engineering, RNNs, DNNs, and classifiers using PyTorch, XGBoost, Pandas) tools.
- Improved Node's capabilities with new models and new features (e.g., LSTM/GRU sequence models, denoising autoencoder models for data enhancement and neural embeddings).
- Consulted on coworker projects and acted as resident research-paper-reader.
Research Assistant/Research Engineer2012 - 2018Oracle Labs
Technologies: Oracle RDBMS, Linux Kernel, C, C++, Linux, TensorFlow
- Expanded the market for Oracle's big data analytics offering by providing cutting-edge fraud detection capabilities.
- Expanded Oracle PGX's analytics market share by building a linearly-scalable asynchronous query engine for its distributed execution mode (see GRADES17 paper).
- Introduced Oracle PGX graph analytics to the big data market by building its first large-scale distributed execution mode in C++ and Node.js.
- Gave fine-grained performance optimization capabilities to the Oracle Database team by writing a custom Linux kernel.
- Provided the option to run Oracle Database in a safe Java-like environment with on-demand profile-guided optimization using a dynamic C/C++ LLVM runtime.
- Demonstrated and designed a new research product for selling big-data tools; a graphical system for creating dataflow graphs.
- Demonstrated Oracle Coherence to customers for writing message-oriented middleware by building a highly scalable Java application server.
- Showcased Oracle Labs' new Truffle/GraalVM compiler technology for optimized, scalable big data operations in legacy languages by writing an HTML5 programmer's notebook in Node.js.