Member of Technical Staff

About Liquid AI

Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there.

The Opportunity

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.

What We’re Looking For

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.

The Work

• 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