Applied Post-Training Engineer at LIQUID AI

Location: San Francisco, California
Type: Full-time
Posted: Jan 23, 2026

Description

Liquid AI, spun out of MIT CSAIL, is seeking an experienced individual to take ownership of applied post-training work for text workloads. This role involves adapting Liquid Foundation Models for large enterprise customers, acting as a technical bridge between customer requirements and model delivery. The successful candidate will lead engagements from scoping through evaluation, build reusable applied workflows and tooling, and focus on data quality, evaluation design, and practical application of language models in production.

Required Skills

Data Generation
Evaluation Design
Instruction Tuning
RLHF
DPO
PyTorch

Additional Information

Required Previous Experiences: Hands-on experience with data generation and evaluation for LLM post-training
Experience training or fine-tuning models using SFT, instruction tuning, RLHF, DPO, or similar preference alignment methods
Strong intuition for text data quality and evaluation design
Experience with text-specific post-training workflows: chat model alignment, instruction tuning, or text data curation at scale
Proficiency with open-source ML ecosystem (Hugging Face, PyTorch) and modern model architectures
Preferred Qualifications: Experience delivering applied ML work to external customers with measurable outcomes
Familiarity with inference optimization frameworks (vLLM, SGLang, TensorRT)
Experience building reusable ML tooling or evaluation infrastructure

About LIQUID AI

LIQUID AI

Information

Liquid AI is an efficiency-first foundation model company. We build highly capable, compute-optimized models that bring intelligence to any device and medium of choice.

Open roles: 8
New in 30 days: 2

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