ML Research Engineer, Comcast
Recommendation and Arbitration team · Washington, D.C.
- LLM post-training for user profile generation: built a teacher-student LLM post-training pipeline on a 3B-parameter student model — SFT with auxiliary reasoning objectives, DPO for alignment, and off-/on-policy evaluation — cascading fine-tuned models as teachers for downstream recommendation tasks.
- LLM data augmentation for cold-start recommendation: designed an LLM-based pipeline injecting synthetic user histories for cold-start items, achieving an 80% relative NDCG improvement while maintaining overall quality (CIKM 2026, EACL 2027).
- Xfinity Campaign Manager — forecasting: built campaign-level time-series forecasting models and ETL pipelines (Spark, Databricks, SQL) across EU and NA markets, with automated dashboarding for weekly media-buy pacing and budget allocation.
- Content discoverability simulator: architected a large-scale simulation using LLM-generated synthetic queries and historical clustering to evaluate upcoming content in the Xfinity TV ecosystem (RecSys 2025).
- Search quality evaluation: deployed a hybrid heuristic–LLM inference system for real-time moderation of 5M+ daily voice queries; introduced a time-aware dynamic lexicon reducing false positives by 35% (SIGIR 2025).