David Yang

Staff Software Engineer

AURORA

Mar 2025 - Current (1 year 5 months)

• Designed and implemented scalable Python-based microservices deployed on GCP leveraging Docker and Kubernetes to support renewable energy optimization workflows, integrating real-time telemetry data with intelligent decision pipelines while ensuring high availability and seamless interoperability with enterprise systems. • Developed advanced LLMs-powered applications utilizing RAG pipelines and vector search backed by BigQuery and Redis, enabling contextual retrieval of operational energy data and improving decision support systems through efficient embedding- based semantic indexing and retrieval. • Engineered agentic Al workflows using LangGraph and AutoGen to orchestrate multi-step reasoning tasks across distributed services, enabling

Senior Applied Scientist II / Senior Software Engineer

Cruise

Aug 2020 - Mar 2025 (4 years 8 months)

• Developed large-scale backend systems using Java and Python within microservices architecture on AWS, supporting data- intensive applications in autonomous mobility platforms with emphasis on scalability, fault tolerance, and efficient API communication. • Built distributed data pipelines leveraging PostgreSQL, Redis, and streaming systems to process real-time vehicle telemetry, enabling robust data ingestion and transformation workflows supporting machine learning and analytics use cases. • Implemented containerized services using Docker and orchestrated deployments via Kubernetes, improving operational consistency and enabling scalable service management across complex distributed environments. • Designed and integrated early-stage LLMs

Software Engineer

Zoox

Jul 2019 - Jul 2020 (1 year 1 month)

• Developed backend services using C++ and Python to support autonomous vehicle systems, focusing on data processing pipelines and efficient communication between distributed modules handling sensor data. • Implemented data storage and retrieval mechanisms using SQL and PostgreSQL, enabling structured access to large-scale datasets required for machine learning model training and validation workflows. • Worked with containerized environments using Docker to ensure consistent development and deployment processes across engineering teams working on autonomous mobility systems. • Supported integration of machine learning components built with TensorFlow and PyTorch into backend systems, enabling real-time inference capabilities for perception

Autonomous Driving Software Engineer

Byton

May 2018 - Jul 2019 (1 year 3 months)

• Developed software components in C++ and Python supporting intelligent vehicle systems, focusing on real-time data processing and integration of sensor inputs for autonomous driving features. • Worked on backend systems utilizing SQL databases to manage structured data used in model training and validation processes for intelligent vehicle platforms. • Integrated machine learning models built with TensorFlow into application workflows, enabling predictive capabilities within connected vehicle systems. • Collaborated on system-level integration ensuring reliable communication between embedded systems and backend infrastructure supporting vehicle intelligence.

S

Machine Learning Engineer

Spark Al

Aug 2017 - Oct 2017 (3 months)

• Developed machine learning models using Python and TensorFlow for marketing analytics applications, focusing on predictive modeling and customer segmentation use cases. • Built data processing pipelines using SQL and ETL workflows to prepare structured datasets for training and evaluation of machine learning models. • Implemented backend services to expose model predictions through APIs, enabling integration with external tools and applications used by marketing teams. • Optimized model performance and data workflows, improving efficiency of training and inference processes within resource- constrained environments.

About

Detail

Timeline

Résumé