Whodat · July 2018 – August 2019
High-Performance AR and Vision
Low-level C++ vision and monocular depth research in an AR startup environment.
Role: Deep Learning Engineer
Executive summary
Built and researched performance-sensitive vision primitives before the team transitioned to Osmo after acquisition.
Problem and constraints
AR systems needed fast feature detection and research depth around monocular depth estimation.
- Runtime performance
- C++ implementation
- Startup ambiguity
- Research-to-product translation
Architecture
Decision Theater
Decision fork
Use standard primitive vs optimize core detector
Vision performance depends on low-level primitives that run constantly.
Chosen: Optimized C++ detector. Low-level performance work compounds across real-time AR pipelines.
Evaluation and reliability
- Benchmarked detector speed against ORB-SLAM baseline.
Observability and debugging
- Performance measurement drove optimization choices.
Reflection
This work gives the portfolio low-level systems depth alongside modern LLM platform work.
This case study uses sanitized architecture and representative examples. It excludes confidential prompts, customer data, proprietary datasets, private implementation details, and internal traces.