Member of Technical Staff - Applied ML, RecSys

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:

• 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

• 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