About Liquid AI
Spun out of MIT CSAIL, we build AI systems that run where others stall: on CPUs, with low latency, minimal memory, and maximum 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 sit at the intersection of frontier foundation models and real-world deployment. You’ll own applied post-training work end-to-end for some of the world’s largest enterprises, while still contributing directly to Liquid’s core model development. Unlike most roles that force a trade-off between customer impact and foundational work, this role gives you both: deep ownership over how models are adapted, evaluated, and shipped, and a direct line into the evolution of Liquid’s post-training stack. If you care about data quality, evaluation, and making models actually work in production, this is a chance to shape how applied AI is done at a foundation-model company.
What We're Looking For
We need someone who:
Takes ownership: Owns post-training projects end-to-end, from customer requirements through delivery and evaluation.
Thinks end-to-end: Can reason across data generation, training, alignment, and evaluation as a single system.
Is pragmatic: Optimises 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.
Translate customer requirements into concrete post-training specifications and workflows.
Design and execute data generation, filtering, and quality assessment processes.
Run supervised fine-tuning, preference alignment, and reinforcement learning workflows.
Design task-specific evaluations, interpret results, and feed learnings back into core post-training pipelines.
Desired Experience
Must-have:
Hands-on experience with data generation and evaluation for LLM post-training.
Experience training or fine-tuning models using SFT, preference alignment, and/or RL.
Strong intuition for data quality and evaluation design.
Familiarity with alignment or RL techniques beyond basic supervised fine-tuning.
Nice-to-have:
Experience contributing to shared or general-purpose post-training infrastructure.
Prior exposure to customer-facing or applied ML delivery environments.
Familiarity with alignment or RL techniques beyond basic supervised fine-tuning.
What Success Looks Like (Year One)
Independently owns and delivers enterprise post-training projects with minimal oversight.
Is trusted by customers as the technical owner, demonstrating strong judgment and delivery quality.
Has made durable contributions to Liquid’s general-purpose post-training pipelines by feeding applied learnings back into baseline model development.
What We Offer
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
