About the job
This is a rare chance to apply frontier sequential recommendation architectures to real enterprise problems at scale. You will own applied ML work end-to-end for recommendation system workloads, adapting Liquid Foundation Models for customers who need personalization and ranking capabilities that run efficiently under production constraints.
Responsibilities
Act as the technical owner for enterprise customer engagements involving recommendation and ranking workloads
Translate customer requirements into concrete specifications for recommendation models
Design and execute data pipelines for user interaction data, feature engineering, and training data curation at scale
Fine-tune and adapt large-scale sequential recommendation models (e.g., HSTU-style architectures) for customer-specific use cases
Design task-specific evaluations for recommendation model performance (ranking quality, latency, throughput) and interpret results
Build reusable applied tooling and workflows that accelerate future customer engagements
Qualifications
Minimum
Hands-on experience building or fine-tuning recommendation models at scale (not just off-the-shelf collaborative filtering)
Experience with sequential recommendation architectures, user behavior modeling, or large-scale ranking systems
Strong intuition for data quality and evaluation design in recommendation contexts (offline metrics, A/B testing, business metric alignment)
Experience with large-scale data pipelines for user interaction data and feature engineering
Proficiency in Python and PyTorch with autonomous coding and debugging ability
Preferred
Experience with transformer-based recommendation architectures (HSTU, SASRec, BERT4Rec, or similar)
Experience delivering recommendation systems to external customers with measurable business outcomes
Familiarity with serving recommendation models under latency and throughput constraints