About The Position
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. The Opportunity 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.
Requirements
- 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 Haves
- 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
Responsibilities
- 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
Benefits
- 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
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
