Member of Technical Staff - Applied ML, RecSys

About the role

Liquid AI builds general-purpose AI systems that run efficiently across various deployment targets, including data center accelerators and on-device hardware. We collaborate with enterprises in sectors such as consumer electronics, automotive, life sciences, and financial services. We are expanding rapidly and seek talented individuals to contribute to our growth.

Opportunity

This role offers the opportunity to apply advanced sequential recommendation architectures to real-world enterprise problems at scale. You will be responsible for the entire lifecycle of recommendation system engagements, from requirements gathering to delivery and evaluation. Unlike typical recommendation roles, this position provides comprehensive ownership over adapting, evaluating, and deploying large-scale recommendation models for enterprise customers.

Between engagements, you will develop reusable tools and workflows to expedite future projects. If you are passionate about data quality, user behavior modeling, and ensuring recommendation systems perform effectively in production environments, this role is ideal for you.

What We’re Looking For

• Takes ownership: Owns customer recommendation system engagements end-to-end, from requirements through delivery and evaluation.
• Think at scale: Can reason about user interaction data, sequential modeling, feature engineering, and evaluation across large-scale production systems.
• Is pragmatic: Focuses on measurable customer outcomes (engagement, conversion, revenue lift) over theoretical novelty.
• Communicates clearly: Can bridge between customer business metrics and internal technical decisions, and push back when necessary.

The Work

• 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.

Experience

• 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.

Success

• Independently owns and delivers enterprise recommendation system engagements with minimal oversight.
• Trusted by customers as the technical owner, demonstrating strong judgment on the tradeoffs between model quality, latency, and business impact.
• Built reusable applied workflows or tooling that accelerate future customer engagements.

What We Offer

• Real ML work: 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: Pays 100% of medical, dental, and vision premiums for employees and dependents.
• Financial: Offers a 401(k) matching up to 4% of base pay.
• Time Off: Unlimited PTO plus company-wide Refill Days throughout the year.