LLMs & GenAI
LLM research and alignment, fine-tuning, language modeling, multimodal systems, synthetic data generation.
AI/ML Engineering Leader · New York Metro
Darian Garcia
I build and lead teams that take machine learning from research to production — from LLM research at Meta and Instagram ranking for 1.3B+ daily users, to computer vision running on satellites in orbit.
Previously MetaInstagramHarvardPrincetonCapital OneCACI
- 10+
- years shipping ML
- 1.3B+
- daily users served
- <100ms
- on-orbit inference
- 4,500+
- mission interventions
01 Experience
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2026 — Present
VP of Engineering, AI/ML
Gladiator AI · Remote
Own the AI/ML engineering function — executive technical vision, hiring and growing the team, and architecting next-generation AI systems from zero to one.
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2025 — 2026
Senior Research Scientist, AI/ML
Meta · New York, NY
- Contributed core LLM research to MuseSpark 1.0–1.3 — model architecture, training efficiency, fine-tuning, and large-scale alignment.
- Led ML design of RecSys infrastructure powering Instagram Stories ranking for 1.3B+ daily users.
- Set multi-quarter technical direction across research and product teams in a large, matrixed organization.
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2023 — 2025
Lead Software Engineer, AI/ML
Scientific Systems · Remote
- Architected a spaceborne compute platform — k3s and Docker on orbital satellites, bringing native Python and ML workloads to space.
- Led edge computer-vision models with a synthetic data pipeline for autonomous, real-time spacecraft identification and tracking.
- Quantized CNN/YOLO to embedded ARM via TensorRT/ONNX — 90% less labeled data at sub-100ms latency.
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2022 — 2023
Senior Software Engineer, AI/ML
Capital One · Remote
- Invented first-of-its-kind load-balancer routing for card transactions, bypassing legacy mainframe rails.
- Deployed real-time credit-risk ML (XGBoost, streaming inference) as scoring microservices at sub-50ms latency.
- Built MLOps observability with statistical anomaly detection across high-volume transactions.
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2019 — 2022
Senior Software Engineer, AI/ML
CACI · Remote
- Built the government's first end-to-end production ML pipeline for intelligence analysis, as customer-facing technical lead.
- Fine-tuned and deployed transformer NLP models (BiBERT, Multilingual BERT) for entity extraction at scale, with CUDA-level optimization.
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2020 — 2021
Teaching Fellow
Harvard University · Harvard Extension School
Mentored the next generation of engineers across Python, modern full-stack architectures, and production-grade machine learning.
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2016 — 2019
Earlier
UMBC — Research Scientist, AI/ML ThreatQuotient — Software Engineer Que Technology Group — Software Engineer
02 Expertise
ML Infrastructure
Distributed training, inference optimization (TensorRT, ONNX), evaluation, MLOps observability.
Systems & Scale
Distributed systems, HPC, CUDA, Kubernetes / k3s / Docker, AWS, petabyte-scale data pipelines.
Leadership
Building teams, multi-quarter roadmaps, design reviews, mentoring, and customer-facing delivery.
03 Projects
Open source · Java
Minimal TAK Server
A security-first, lightweight server for the Team Awareness Kit (TAK) ecosystem with a deliberately small attack surface: protobuf-only Cursor-on-Target messaging, mutual TLS on every connection, Netty for TCP and Spring Boot for a REST API with health and metrics endpoints. Ships ready to run in a container.
View on GitHub ↗04 Education
- M.S., Artificial Intelligence4.0 GPAUMBC
- MBA, Information TechnologyExecutive leadership, strategy, and corporate financeQuantic
- B.S., Computer ScienceMagna cum laude in 3 years · 4.0 in major · Public-interest technology fellowUMBC
- Graduate Coursework, Software Engineering4.0 GPAHarvard
- Fine Arts & Humanities SymposiumSelected participant: ethics, policy, and the humanitiesPrinceton
AWS Certified ML Specialty · AWS Solutions Architect Associate · Microsoft MLOps Engineer Associate · Certified Scrum Master
Let's build something.
Open to conversations about AI/ML leadership, advising, and interesting problems.
hello@dariangarcia.dev