About Liquid AI

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 own applied post-training work end-to-end for text workloads, adapting Liquid Foundation Models for some of the world’s largest enterprise customers.

You will act as the technical bridge between customer requirements and model delivery. You will lead engagements from scoping through evaluation, with full ownership over how text models are adapted and shipped. Between engagements, you will build reusable applied workflows and tooling that accelerate future delivery.

If you care about data quality, evaluation design, and making language models actually work in production for real customers, this is the role.

What We're Looking For

We need someone who:

• Takes ownership: Owns customer post-training projects end-to-end, from requirements through delivery and evaluation.
• Thinks end-to-end: Can reason across data generation, instruction tuning, alignment, and evaluation as a single system.
• Is pragmatic: Optimizes for model quality and customer outcomes over publications or theory.
• Communicates clearly: Can translate between customer needs and internal technical teams, and push back when needed.
  

The Work

• Act as the technical owner for enterprise customer post-training engagements involving text workloads
• Translate customer requirements into concrete post-training specifications and workflows
• Design and execute data generation, filtering, and quality assessment processes for text corpora
• Run supervised fine-tuning, instruction tuning, RLHF, DPO, and other preference alignment workflows
• Design task-specific evaluations for text model performance and interpret results
• Build reusable applied tooling and workflows that accelerate future customer engagements