other
Posted 3 days agoMember of Technical Staff Applied ML RecSys
at Liquid AI
Remote
Responsibilities
- 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 Desired Experience Must-have: • Hands-on
Requirements
- About Liquid AI The Opportunity
- 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.
- Experience with sequential recommendation architectures, user behavior modeling, or large-scale ranking systems •
- Experience with large-scale data pipelines for user interaction data and feature engineering •
- Proficiency in Python and PyTorch with autonomous coding and debugging ability Nice-to-have: •
- Experience with transformer-based recommendation architectures (HSTU, SASRec, BERT4Rec, or similar) •
- Familiarity with serving recommendation models under latency and throughput constraints
- Real ML work: You will build and adapt large-scale recommendation models for enterprise customers, working with frontier architectures like HSTU under real production constraints. •
- This is a beta feature to avoid spam applicants.
Benefits
- Compensation: Competitive base salary with equity in a unicorn-stage company •
- Health: We pay 100% of medical, dental, and vision premiums for employees and dependents •
- Financial: 401(k) matching up to 4% of base pay •
- Time Off: Unlimited PTO plus company-wide Refill Days throughout the year