Hiring: Member of Technical Staff - Applied ML, RecSys Company: Liquid AI Location: Boston, MA Job Posted Time: 2026-09-03 10:30:52 Target Skills & Keywords : A/B Testing, Data Pipeline, Fine-tuning, PyTorch, Python About the job Experience: •Hands-on experience building or fine-tuning recommendation models at scale (not just off-the-shelf collaborative filtering) •Strong intuition for data quality and evaluation design in recommendation contexts (offline metrics, A/B testing, business metric alignment) •Proficiency in Python and PyTorch with autonomous coding and debugging ability Required Skills: •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. •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 Qualifications: •Operational familiarity with serving recommendation models under latency and throughput constraints •What Success Looks Like (Year One) •Independently owns and delivers enterprise recommendation system engagements with minimal oversight •Is trusted by customers as the technical owner, demonstrating strong judgment on the tradeoffs between model quality, latency, and business impact •Has built reusable applied workflows or tooling that accelerate future customer engagements Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!