I'm a Member of Technical Staff on the Reasoning team at xAI, where I lead search and factuality post-training for the Grok model family - powering real-time capabilities of Grok. In addition, I led the fast/instant mode training for model in grok consumer app in early half of 2026.
Previously I spent five years at Apple as a ML Engineering Manager in search and language modeling, and before that I was an Applied Scientist at Uber building fraud detection systems and a data scientist at LeanTaaS building scheduling systems.
Experience
Google DeepMind
Senior Staff Research Scientist - Long horizon RL
xAI
Grok 4.5:
Large scale search & factuality data generation for SFT/Mid-training.
Grok 4.20 / Grok 4.3:
Led Grok 4.20 / Grok 4.3 instant/minimal-reasoning mode post-training.
Responsible for Grok chat model post-training; led search & factuality post-training for multi-agents.
Grok 4.20 (Arastradero) beta-1 ranked #1 in Search Arena upon launch.
Grok 4.20 / Grok 4.3 reduced 80%+ factual errors from Grok 4 Fast when both equipped with browsing and search tools.
Grok 4.1 & Fast:
Led search & factuality post-training, reducing hallucinations by 70% in instant mode.
Grok 4.1 Fast non-reasoning with agentic search powers Grok in Tesla, helping users seek information and navigate in seconds.
Grok 4 Fast:
Led large-scale search RL and SFT.
Grok 4 Fast (Menlo) ranked #1 in Search Arena upon launch and achieved Pareto-frontier intelligence.
Grok 4:
Core contributor to Grok 4.
Created the very first set of synthetic data that makes agentic search/X search work.
Apple
Trained sub-15ms efficient language models that correct spelling, auto-complete, and query understanding for 1 billion users across various apps.
Uber
Built real-time ML systems that cut fraud losses by 60+% at a third of the action rate.
LeanTaaS
Built the optimization algorithm for scheduling and wait-time optimization serving the top 20 hospitals in the United States, cutting wait times by 30% during peak hours.