Alex Popov

Architected distributed Go and gRPC backend services for HashiCorp Cloud Platform (HCP) Packer, powering secure machine-image lifecycle management. Spearheaded SBOM to improve compliance visibility by 65% and instrumented OpenTelemetry to cut incident resolution time by 50%.

Lead research and development of deep learning solutions for Autonomous Vehicles including pedestrian detection, scene segmentation, obstacle perception, neural planning, and world models to improve perception and planning robustness.

Orchestrated backend architecture for HashiCorp Cloud Platform (HCP) Terraform, enabling multi-cloud infrastructure automation across AWS, Azure, and GCP. Launched Terraform Stacks to reduce enterprise provisioning time by 35% and improved deployment transparency by 30%, while expanding automated testing to increase deployment stability by 35%.

Developed deep learning and computer vision systems for the Autonomous Vehicles group, delivering models and pipelines for perception and sensor fusion projects.

Directed frontend architecture for Crocs DTC commerce platform, delivering product discovery, cart, and checkout across multi-region storefronts. Reduced defect rates by 30% and accelerated feature delivery by 25% via modular React systems, while cutting backend response latency by 28% through Redis caching and asynchronous orchestration.

Researched and implemented deep learning and computer vision solutions for Surface Applied Sciences including object detection, person identification, and speaker identification; contributed to system engineering and HW/SW integration.

Advanced DroneDeploy mapping and aerial analytics platform to help enterprises analyze drone-captured geospatial imagery at scale. Built image ingestion and terrain modeling pipelines in Python and Node.js, reducing processing turnaround time by 35%, and standardized reusable UI patterns to minimize regression defects.

Built Android Wear experiences and applied machine learning to sensor data to enable intelligent wearable scenarios and new user interactions.

Powered backend systems for Airbnb booking and availability platform, supporting booking validation and messaging workflows used by millions globally. Reduced booking flow latency by 20% through improved Redis caching and database indexing, and delivered responsive booking and calendar experiences across desktop and mobile.

Developed big-data and machine-learning based knowledge services for the Commerce Platform to support Microsoft consumer and commercial products.

Bachelor of Science in Computer Science, completed from 2010 to 2014 in San Diego, California.

Completed a Master of Science in Computer Science with coursework and research in areas relevant to machine learning and computer vision.

Earned a Bachelor of Science in Computer Science with a second major or strong coursework in Philosophy.