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