S

Steven Wong

About

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Machine Learning Engineer
United States

Timeline


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Résumé


Jobs verified_user 0% verified
  • Plaid
    Senior ML Engineer
    Plaid
    May 2025 - Jan 2026 (9 months)
    • Spearheaded the design and deployment of highly scalable, production-grade AI/ML systems within a complex enterprise environment, specifically blending cutting-edge AI with human-in-the-loop validation for enhanced precision. • Developed and fine-tuned advanced large models, including bespoke LLMs and sophisticated computer vision architectures (CNNs, Transformers), utilizing PyTorch 2.x and TensorFlow 2.x on accelerated NVIDIA CUDA infrastructure. • Architected Retrieval-Augmented Generation (RAG) pipelines, leveraging Hugging Face Transformers and spaCy, optimizing information retrieval and generation quality for client-facing applications. • Managed the entire ML lifecycle (MLOps) using Databricks and implemented robust CI/CD pipelines
  • Helm.ai
    Senior Machine Learning Engineer
    Helm.ai
    Mar 2023 - Jun 2025 (2 years 4 months)
    • Spearheaded the MLOps infrastructure development for predictive analytics models critical to maintaining real-time grid reliability and optimizing complex energy market operations within the ISO/RTO regulatory framework. • Designed and implemented robust, scalable CI/CD pipelines using Jenkins and GitLab CI/CD for ML model versioning and automated deployment, integrating with enterprise artifact repositories like Nexus and Artifactory. • Managed containerized ML workloads using Docker and orchestrated production deployments across hybrid cloud infrastructure (AWS and on-premise private cloud), leveraging Kubernetes (K8s) and Helm for high availability. • Developed comprehensive monitoring and observability stacks utilizing Prometheus, Gra
  • M
    Machine Learning Engineer
    Motion2AI
    Jan 2022 - Mar 2023 (1 year 3 months)
    • Led the development and deployment of end-to-end AI-driven predictive modeling solutions for applications, navigating stringent regulatory compliance requirements. • Architected robust data pipelines for handling highly data in compliance with regulatory standards, using Python 3.9+, Pandas, and NumPy in environments utilizing Java and C# components. • Developed and optimized deep learning models (CNNs, RNNs, Transformers) using PyTorch and TensorFlow 2.x. • Implemented Natural Language Processing (NLP) techniques using Hugging Face Transformers and spaCy to extract actionable insights. • Managed the end-to-end Machine Learning lifecycle (MLOps) using MLflow for experiment tracking and model registry, ensuring reproducibility and audit tr
  • Los alamos National laboratory
    Machine Learning Engineer
    Los alamos National laboratory
    Jun 2021 - Dec 2021 (7 months)
    • Engineered and deployed production-grade machine learning models for key financial services, including fraud detection and customer experience personalization, within AWS cloud-native environment. • Leveraged Python 3.9+ with advanced ML frameworks like TensorFlow 2.x and PyTorch 1.x (version 1.9+ active during this period) on a high-scale, event-driven architecture processing real-time customer transactions. • Pioneered a serverless-first architecture using AWS Lambda (Python runtime), Amazon Kinesis, and AWS Step Functions, enabling highly responsive and scalable ML solutions. • Managed the entire ML lifecycle (MLOps) using Rubicon-ml, ensuring experiment tracking, auditability, and reproducibility aligned with governance requirements.
  • UC San Diego
    Senior Data Engineer
    UC San Diego
    Jun 2020 - May 2021 (1 year)
    • Developed robust, distributed data pipelines for a centralized data lake, performing complex data manipulation, modeling, and transformation using Python 3.8+, SQL, and Apache Spark on the Databricks platform. • Processed petabyte-scale datasets stored in Amazon S3, contributing to a scalable cloud-native data warehousing architecture (Snowflake or similar solutions). • Implemented core MLOps principles across the ML lifecycle, focusing on infrastructure development and automation to ensure reproducible and scalable AI systems. • Containerized ML applications using Docker and managed scalable orchestrations within Kubernetes (K8s) clusters (transitioning to Amazon EKS), optimizing architecture for high performance computing (HPC) requirem
  • U
    Data Engineer
    USCD MEDICAL OFFICES SOUTH
    Sep 2016 - Jun 2020 (3 years 10 months)
    • Clinical Data Architecture: Designed and maintained scalable clinical data warehouses using PostgreSQL 9.6/10 and SQL Server 2016, centralizing patient records for over 50,000+ monthly visits while ensuring strict HIPAA and HITECH compliance. • ETL Pipeline Automation: Engineered high-reliability ETL workflows using Python 2.7/3.6 and Apache Airflow 1.10 to ingest and normalize disparate data from Epic EHR (Electronic Health Records) and legacy billing systems. • Healthcare Interoperability: Implemented data exchange protocols using HL7 v2 and FHIR (Fast Healthcare Interoperability Resources) standards, enabling seamless data sharing between specialized clinics and the broader UC San Diego Health network. • Predictive Patient Analytics: P
Education verified_user 0% verified
  • UC San Diego
    Master's Degree in Machine Learning and Data Science
    UC San Diego
    Jan 2020 - Jan 2021 (1 year 1 month)
  • UC San Diego
    Bachelor's Degree in Electrical and Computer Engineering
    UC San Diego
    Jan 2017 - Jan 2020 (3 years 1 month)