Member of Technical Staff Applied ML RecSys

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

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. •
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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

This is a rare chance to apply frontier sequential recommendation architectures to real enterprise problems at scale.
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.
Takes ownership: Owns customer recommendation system engagements end-to-end, from
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 •