Member of Technical Staff - Applied ML, RecSys

Liquid AI • Cambridge, MA

1d

About The Position

Liquid AI is seeking a Member of Technical Staff to own applied machine learning work end-to-end for enterprise recommendation system workloads. The role adapts Liquid Foundation Models for customer personalization and ranking needs, with a focus on production efficiency and measurable business outcomes. It includes customer-facing technical ownership as well as building reusable applied tooling and workflows between engagements.

The position involves translating customer requirements into model specifications; creating data pipelines and training datasets from user interaction data; adapting large-scale sequential recommendation models; and evaluating ranking quality, latency, and throughput. The successful candidate will work across customer business metrics and technical decisions, balancing model quality, production constraints, and business impact.

Requirements

• Hands-on experience building or fine-tuning recommendation models at scale, beyond 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, including offline metrics, A/B testing, and business metric alignment.
• Experience with large-scale data pipelines for user interaction data and feature engineering.
• Proficiency in Python and PyTorch, with the ability to code and debug autonomously.

Nice To Haves

• Experience with transformer-based recommendation architectures, such as 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, such as HSTU-style architectures, for customer-specific use cases.
• Design task-specific evaluations for recommendation model performance, including ranking quality, latency, and throughput, and interpret results.
• Build reusable applied tooling and workflows that accelerate future customer engagements.

Benefits

• Competitive base salary with equity in a unicorn-stage company.
• 100% of medical, dental, and vision premiums paid for employees and dependents.
• 401(k) matching up to 4% of base pay.
• Unlimited PTO and company-wide Refill Days throughout the year.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed