Christopher Mejo, Ph.D.
Resume PDF
New York, NY • Open to Principal / Staff AI Research & Systems Roles

Christopher Mejo, Ph.D.

AI Research Scientist — LLM & Reinforcement Learning Systems

15+ years taking machine learning systems from mathematical theory to high-throughput production. Ph.D. in Physics from Harvard University (quantum complexity theory, completed at age 21) and postdoctoral fellow in theoretical computer science at MIT under Scott Aaronson.

I architect large language model systems (hybrid retrieval, distributed pretraining, agentic reasoning) and sequential decision-making engines—owning systems end-to-end from custom CUDA kernels to live production deployment.

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Production Experience
15+ yrs
End-to-end ML & distributed systems
Open-Source RAG Scale
15M+ papers
AI Scholar (GSoC Fellow ’24 & ’25)
Inference Throughput
1B+ events/day
Microsecond-latency transformers
Academic Pedigree
Harvard & MIT
Physics Ph.D. at 21 • Active TS/SCI

Selected Systems

Core Architecture & Research Focus
Open Source • GSoC ’24 & ’25 GitHub ↗

AI Scholar Hybrid RAG

Tri-modal retrieval engine combining dense vectors (FAISS), lexical search (BM25), and citation knowledge graphs (Neo4j) across 15M+ scientific papers, with MAML meta-learned personalization and calibrated uncertainty.

+40% NDCG@10 vs. baseline

Foundation Models

Distributed LLM Training

Multi-GPU pretraining and instruction fine-tuning pipelines for 7B–14B parameter models using DeepSpeed ZeRO-3 and custom CUDA memory-mapped tokenizers, paired with enterprise RAG over 10M+ filings.

−38% per-step wall-clock latency

Sequential Decision-Making

Real-Time RL & Transformers

Deep Q-network, actor-critic, and Heterogeneous Graph Attention (HAN) systems operating on non-stationary streaming data at microsecond latency across 1B+ daily market events.

6.2 live Sharpe (24-mo continuous)

Industry Experience

Full Resume (PDF)

Senior Data Scientist

Braverock LLC
  • Distributed LLM Training: Directed pretraining and instruction fine-tuning for domain-specific 7B–14B foundation models (DeepSpeed ZeRO-3, custom CUDA memory-mapped tokenizers), cutting per-step latency 38%.
  • Enterprise RAG (10M+ Docs): Engineered a production retrieval pipeline over SEC filings, earnings transcripts, and research reports using custom domain embeddings and vector indexing.
  • Sub-Millisecond Transformers: Deployed time-series transformer models processing 1B+ quote events per day at microsecond-scale inference latency.
  • Reinforcement Learning Execution: Architected DQN and actor-critic portfolio optimization agents, improving live and backtested Sharpe ratio 23% over the prior production baseline.
  • Graph Attention Networks: Built dynamic Heterogeneous Graph Attention Networks (HAN) capturing multi-horizon cross-asset dependencies, raising regime-shift detection accuracy 19%.
  • Live Production Performance: Delivered a 6.2 Sharpe ratio (Sortino 8.4, max drawdown <1.9%) over a 24-month continuous live window executing billions in volume, plus a 2.63-Sharpe beta-neutral strategy on $60M+ AUM.

Senior Data Scientist

Cushion AI
  • Automated Negotiation LLM: Created CushionNLP, a proprietary BERT-based dialogue agent that increased bank-fee negotiation success 30% and generated $2M+ in annual customer savings.
  • Graph Transaction Classification: Architected a GNN and ensemble categorization pipeline across 80M+ transactions (~300 institutions), reaching 95% accuracy and halving runtime vs. legacy rule engines.

Research Data Scientist

RapidRads AI
  • Federated Clinical Computer Vision: Deployed a differentially private (ε = 1.2, δ = 10⁻⁵) EfficientNet-B7 diagnostic system across 4 hospitals, achieving 0.972 ROC-AUC (96.5% sensitivity, 98.2% specificity).
  • Clinical Interpretability: Integrated multi-CNN ensembles with Grad-CAM saliency attribution and calibrated uncertainty quantification for radiologist validation.

Senior Data Scientist & Cybersecurity Researcher

Confiant
  • Adversarial & Botnet Detection: Engineered GraphSAGE relational models and unsupervised anomaly detectors (LSTM autoencoders, Isolation Forest, One-Class SVM) securing 1B+ daily ad requests against coordinated attacks.

CEO & Lead Architect

Cronode
  • Search & Recommendation Infrastructure: Designed hybrid collaborative-filtering recommendation engines and custom Apache Solr ranking architectures from the ground up.

Research Fellowships & Open Source

Concurrent Appointments

Google Summer of Code Fellow (2024 & 2025)

R Foundation for Statistical Computing — AI Scholar
  • Hybrid Graph-Vector Retrieval: Solo-architected an open-source scientific RAG platform uniting FAISS dense vectors, BM25 sparse search, and Neo4j knowledge graphs across 15M+ papers.
  • Meta-Learned Personalization: Designed an adaptive retrieval router using MAML meta-learning, lifting NDCG@10 by 40% on a 200-query citation-grounded benchmark.
  • Verifiable Agentic Reasoning: Implemented chain-of-thought synthesis with strict citation attribution, calibrated epistemic uncertainty (MC dropout, evidential deep learning), and automated entity-relation extraction (spaCy, BERT-NER).

Visiting Research Scientist (Part-Time)

Sandia National Laboratories & Google Quantum AI Initiative
  • Variational Quantum Algorithms: Senior researcher in a joint Google Quantum AI initiative developing variational quantum eigensolvers (VQE) for non-Abelian lattice gauge theory.
  • Quantum Error Correction & HPC: Designed surface-code protocols and resource estimators in Qiskit and Cirq; executed 50+ qubit tensor-network simulations on SLURM clusters and co-authored DARPA/IARPA proposals.

Education & Clearance

  • Massachusetts Institute of Technology (MIT) 2010 – 2012
    Postdoctoral Fellowship, Theoretical Computer Science
    Advisor: Dr. Scott Aaronson
  • Harvard University 2007 – 2010
    Ph.D. in Physics (Completed at Age 21)
    Quantum Information & Complexity Theory
  • Harvard University 2004 – 2007
    B.S. in Physics & Computer Science
  • U.S. Security Clearance Active
    Top Secret / Sensitive Compartmented Information (TS/SCI)

Technical Stack

Languages & Kernels
Python, C++, C, CUDA, R, x86 Assembly, Java, JavaScript
LLM & RAG Architecture
Dense/Sparse/Graph RAG, FAISS, BM25, Neo4j, vLLM, LangGraph, LangChain, Custom Embeddings, BERT-NER, Chain-of-Thought, Uncertainty Quantification (MC Dropout, Evidential DL)
Deep Learning & RL
PyTorch, JAX, TensorFlow, Transformers, DQN, Actor-Critic, MAML Meta-Learning, Graph Neural Networks (HAN, GraphSAGE)
Distributed Systems & MLOps
DeepSpeed ZeRO-3, Multi-GPU Pretraining, Ray, SLURM, Kubernetes, Docker, Kubeflow, MLflow, Airflow, W&B
Quantum & Privacy
Differential Privacy (ε-δ), Federated Learning, Qiskit, Cirq, Surface Codes, VQE

Get in Touch

Available for Principal / Staff AI Research Scientist, LLM Systems Architect, and Founding ML Lead roles (New York, Hybrid, or Remote).

[email protected] Resume (PDF) LinkedIn GitHub