Meta/Senior Machine Learning Engineer · Reality Labs
Burlingame, California
·
Generative AI · Computer Vision · 3D
Generative AI
Synthetic Data
Sim-to-Real
3D Reconstruction
Diffusion Models
DECA
PyTorch3D
Unity
Production ML
Engineering lead on foundational generative AI and computer vision systems deployed at consumer scale — including a production multi-frame facial perception model, a Unity-based synthetic data generation infrastructure, and a diffusion-based model for generating 3D character animations from natural language. Systems deployed to 1.6 billion+ users across Facebook, Instagram, WhatsApp, and Horizon.
1.6B+
Facebook · Instagram · WhatsApp · Horizon
12×
Real-time inference speedup
- Word-to-Animation (2026, current) — tech lead on diffusion-based model generating 3D character animations from natural language; owned eval framework, training infra, and end-to-end pipeline.
- Autogen (2023 — 2025) — multi-phase generative facial perception initiative for Parametric Avatar: Multi-Task Model → 3D reconstruction for parametric faces → Multi-frame Autogen. Outperformed prior production system on all 92 blendshapes — +33% L1, +1.3% global completion, 1.5M+ additional successful generations/yr.
- 12× inference speedup on production facial expression model serving 1.5B+ users — 10.0s → 0.8s for a 300-frame sequence.
- Synthetic data pipeline — Unity-based procedural face generation; 15M+ labeled images. Foundational training infrastructure that powered every phase of Autogen and downstream generative models.
- Primary oncall at billion-user scale across 2023–2025; led incident review for outage affecting 23,000 user requests.
Platform · Delivery
1.6B+Consumer users
4 appsFacebook · Instagram · WhatsApp · Horizon
Synthetic data infrastructure
DECA feature extraction
Multi-frame 3D reconstruction
Production release + telemetry
Model changes
+33%Accuracy lift@ Multi-pose L1
+1.3%Global completion+1.5M generations/yr · at 1.6B scale
−4%Failure reduction@ Pipeline rejections
12×Inference speedup10.0s → 0.8s
- Word-to-Animation (2026, current) — Tech lead for modeling on a diffusion-based product generating 3D character animations from natural language. Established the automated-evaluation framework, training infrastructure, and end-to-end multi-model pipeline.
- Autogen (2023 — 2025) — multi-phase generative facial perception initiative for Parametric Avatar. Took it through three phases: Multi-Task Model → 3D reconstruction for parametric faces → Multi-frame Autogen for parametric avatar. Finetuned the DECA encoder as feature extractor and designed a novel per-frame aggregator over shape/pose parameters; outperformed the prior production system on all 92 blendshapes. Drove +33% L1 on the multi-pose benchmark and +1.3% global completion — delivering 1.5M+ additional successful generations per year and a −4% reduction in pipeline rejections, ~7× the previous model's lift.
- Synthetic data pipeline — foundation for all Autogen work. Designed and built the Unity-based synthetic data generation pipeline for training large-scale facial perception models — 15M+ labeled images via procedural face generation with controlled variation across lighting, pose, and expression. Every phase of Autogen depended on this infrastructure, and it was reused across downstream generative models.
- FACS-based synthetic expression generation pipeline with learned domain transfer across a diverse identity space — adopted as training infrastructure for downstream generative animation models.
- 12× inference speedup on a production facial expression model serving 1.5B+ users — 10.0s → 0.8s for a 300-frame sequence, removing ~92% of critical-path latency.
- Shipped end-to-end model lifecycle for parametric facial reconstruction — owned training, evaluation, internal testing, A/B experimentation, release, fine-tuning, and post-launch telemetry.
- Production ownership at billion-user scale — primary oncall across 2023–2025; led incident review for a high-priority production incident affecting 23,000 user requests.
- Cross-functional collaboration at Meta scale — partnered with Technical Artists, Product Engineers, Production Engineers, and other MLEs across Reality Labs and adjacent organisations to take foundational models from prototype to consumer-product deployment.
- Lead engineer on multiple high-visibility product releases and executive-level technical demonstrations.