Liquid AI

Member of Technical Staff - Applied ML, RecSys

Boston · Staff+

Sponsorship not specifiedDetected 115 days ago

PythonMachine LearningPyTorchData EngineeringNLPA/B Testing

About the role

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.
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.

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.
Between engagements, you will build reusable applied tooling and workflows that accelerate future delivery.
Design and execute data pipelines for user interaction data, feature engineering, and training data curation at scale
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
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.

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

Experience with transformer-based recommendation architectures (HSTU, SASRec, BERT4Rec, or similar)
Familiarity with serving recommendation models under latency and throughput constraints
Has built reusable applied workflows or tooling that accelerate future customer engagements

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

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
Time Off: Unlimited PTO plus company-wide Refill Days throughout the year

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.
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.

This listing is sourced directly from Liquid AI's careers page and normalized into a canonical job model.