Liquid AI The Member of Technical Staff - Applied ML, RecSys at Liquid AI owns applied ML work end-to-end for recommendation system workloads. They adapt Liquid Foundation Models for personalization and ranking under production constraints.
Responsibilities
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.
Unlike most recommendation roles that are siloed into a single product surface, this role gives you full ownership over how large-scale recommendation models are adapted, evaluated, and deployed for enterprise customers. Between engagements, you will build reusable applied tooling and workflows that accelerate future delivery.
If you care about data quality at scale, user behavior modeling, and making recommendation systems actually work in enterprise production environments, this is the role.
Qualification
We need someone whoTakes ownershipThinks at scaleIs pragmaticCommunicates clearlyDesired Experience
Required
We need someone who:
Takes ownership: Owns customer recommendation system engagements end-to-end, from requirements through delivery and evaluation.
Thinks at scale: Can reason about user interaction data, sequential modeling, feature engineering, and evaluation across large-scale production systems.
Is pragmatic: Optimizes for measurable customer outcomes (engagement, conversion, revenue lift) over theoretical novelty.
Communicates clearly: Can translate between customer business metrics and internal technical decisions, and push back when needed.
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
