Member of Technical Staff - Applied ML, RecSys
Core
Own applied ML work end-to-end for recommendation system workloads, adapting Liquid Foundation Models for enterprise customers needing personalization and ranking under production constraints.
Role type
Senior IC applied machine learning engineer (sequential recommendation)
Builds
Reusable applied tooling and workflows for enterprise recommendation engagements
Domain
Enterprise consumer electronics, automotive, life sciences, financial services
Deliverable
production ML models
Required skills
sequential recommendation architectures, user behavior modeling, large-scale ranking systems, data quality and evaluation design, large-scale data pipelines, Python, PyTorch
Preferred skills
transformer-based recommendation architectures (HSTU, SASRec, BERT4Rec), delivering recommendation systems to external customers, serving models under latency and throughput constraints
Responsibilities
Act as 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 and feature engineering, fine-tune and adapt large-scale sequential recommendation models, design task-specific evaluations for recommendation model performance, build reusable applied tooling and workflows
Seniority
Senior, hands-on IC