Member of Technical Staff
Liquid AI
Member of Technical Staff
Liquid AI
Provides generative, edge-capable foundation models
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.
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.
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
Must-have:
Hands-on experience building or fine-tuning recommendation models at scale (not just off-the-shelf collaborative filtering)
Experience with sequential recommendation architectures, user behavior modeling, or large-scale ranking systems
Strong intuition for data quality and evaluation design in recommendation contexts (offline metrics, A/B testing, business metric alignment)
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)
Experience delivering recommendation systems to external customers with measurable business outcomes
Familiarity with serving recommendation models under latency and throughput constraints
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
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.
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
Liquid AI builds and deploys foundation models based on liquid neural networks, focusing on efficient, on-device AI. It develops Liquid Foundation Models (LFMs), a family of generative AI models designed to be smaller and more computation-efficient than typical large language models, enabling deployment on edge devices with lower latency, better privacy, and reduced infrastructure costs. The approach includes end-to-end AI expertise and customizable architectures for enterprises that require real-time performance and private processing. Compared to traditional AI providers, Liquid AI emphasizes edge-ready, adaptable models that run efficiently on constrained hardware, and it targets enterprise-grade, private, reliable AI solutions. The company’s goal is to enable real-time, on-device AI at scale for businesses by offering compact, capable foundation models and the tools to customize them for specific applications.
Explore more opportunities at Liquid AI