Research Engineer · ML Systems & Reinforcement Learning
I build and ship ML systems — from RL and contrastive learning to multi-modal models and LLM evaluation — that run in production at the scale of hundreds of millions of users.
I am a research engineer who builds ML systems that ship — and stay in production. Over the past decade I've taken methods from reinforcement learning, self-supervised learning, and multi-modal modeling out of papers and into systems serving hundreds of millions of users.
My core technical interests span reinforcement learning and preference optimization (contextual bandits, policy optimization, DPO/GRPO, offline policy evaluation), conversational dialogue systems, self-supervised and contrastive representation learning, and active / human-in-the-loop systems for data-efficient training. I care equally about the modeling and the infrastructure that makes fast, rigorous experimentation possible.
At Airbnb I work on machine learning for search, relevance, and personalization, spanning reinforcement learning, preference optimization, and generative AI. At Netflix I pioneered the contrastive learning framework that became the foundation for several production teams, and built training infrastructure that 4×'d experimentation velocity. At Apple I shipped neural query-understanding models across every Apple device and patented a language model for speech endpoint detection.
I earned my Ph.D. in Language Technologies from Carnegie Mellon University, advised by the late Jaime Carbonell. My dissertation studied robust learning under highly-skewed category distributions, and I TA'd the graduate Machine Learning course, teaching core RL concepts — MDPs, policy gradients, value iteration, Q-learning.
I've also founded two AI companies, which means I'm comfortable owning a problem end-to-end: research, implementation, evaluation, and the messy realities of deployment. My first-author work has appeared at NeurIPS, ECML, and AAAI.
Airbnb
Research / Machine Learning Engineer — Relevance & Search
Jori AI
Co-Founder / CEO
Netflix
Research Scientist — Recommendations & Search
Alkymi
Co-Founder / CTO
Apple
Research Scientist — Siri & Apple Search
I
RL & Preference Optimization
Contextual bandits, policy optimization, and preference methods like DPO and GRPO, plus offline policy evaluation.
II
Conversational Systems
Dialogue and intent understanding — from query modeling for Siri and Apple Search to LLM-driven conversational agents.
III
Active Learning
Human-in-the-loop systems and uncertainty sampling for data-efficient, continuously improving models.
IV
Self-Supervised Learning
Contrastive methods and metric learning that power personalization without exhaustive labels.
V
Transfer & Adaptation
Transfer learning and domain adaptation for robust models across shifting data distributions.
VI
Distributed Training
Large-scale distributed training and optimization to accelerate experimentation velocity.
Publications & Patents
Context-Based Endpoint Detection
US Patent 20160358598 · Apple, 2015
A novel approach to speech boundary detection using contextual language models.
Contrastive learning for personalization
NeurIPS Workshop
Self-supervised methods behind "Because You Watched" and "More Like This" at Netflix.
First-author research
ECML · AAAI
Peer-reviewed work on robust and data-efficient machine learning.
Technical Expertise
Methods
Deep Learning
Languages
Distributed Systems
Cloud & Infrastructure
Oct 2023
Sustainability Solutions with AI
Panel Speaker
Feb 2022
AI Solution in Climate
Guest Speaker, Birol Guven's course
Jul 2021
"Your AI or Our AI"
Invited Speaker, Festival of Curiosity
Dec 2020
Similarity at Netflix
NeurIPS Expo
May 2019
Netflix PRS Conference
Invited Speaker
"I'm always happy to talk shop with people building frontier ML systems — research, engineering, or the hard parts in between."