---
title: "Meta — Codec Avatars"
scope: "Sr. Software Engineer, Codec Avatar Quality Evaluation — Reality Labs Research (2020–2022, via ICONMA)"
---

# Meta — Codec Avatars

Sr. Software Engineer, Codec Avatar Quality Evaluation — Reality Labs Research (2020–2022, via ICONMA)

## Receipts

- Evaluation time — 2 hours saved per evaluation (restructured the avatar quality evaluation process)
- Team evaluation capacity — scaled 200% (the direct result of that restructure)

## The challenge

Codec Avatars — Meta's photorealistic telepresence research — needed quantitative and qualitative evaluation of the avatar pipeline at research pace. Evaluators needed tools that didn't exist, and the evaluation process itself was the bottleneck between researchers and a better avatar.

## The decisions — and why

**Decision:** Build the evaluators' tools in both VR and 2D (Unity/C#), rather than a single surface.
**Why:** Defect detection is medium-dependent — some avatar defects only read in VR, others faster on flat screens.
**Tradeoff accepted:** [PLACEHOLDER — authoring session]

**Decision:** Rebuild the team's components as event-driven, decoupled infrastructure before scaling the evaluation process.
**Why:** A highly-interdependent component set can't be scaled or threaded; independence made the restructure possible at all.
**Tradeoff accepted:** [PLACEHOLDER — authoring session]

**Decision:** Co-lead a PyTorch machine-learning initiative to automate quality evaluation.
**Why:** Human evaluation throughput has a ceiling; automated testing scales past it.
**Tradeoff accepted:** [PLACEHOLDER — authoring session]

## The outcome — with numbers

- 2 hours saved per evaluation — the process restructure, in production
- 200% increase in evaluation scaling capacity
- Automated data-collection tools (in-engine + native C++ plugins) delivering debugging information directly to researchers