Member of Technical Staff - Post Training, Applied

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.

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.

We need someone who:

• 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

• 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

• Independently owns and delivers enterprise post-training projects for text workloads with minimal oversight

• Is trusted by customers as the technical owner, demonstrating strong judgment and delivery quality

• Has built reusable applied workflows or tooling that accelerate future customer engagements