Member of Technical Staff - Forward Deployed Engineer

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.

You will work directly on our customer engagements that generate revenue. This is hands-on technical work: fine-tuning Liquid Foundation Models (LFMs) for enterprise deployments across text, vision, and audio modalities. You will own the technical delivery for customers working with their data and constraints to hit quality and latency targets. This is not wrapper work around someone else's API. You will fine-tune models, generate training data, and deploy to devices with real latency and memory constraints.

We need someone who:

• Owns execution: You take a customer use case from technical discovery through delivery, keeping metrics in sight throughout

• Debugs methodically: When a model under performs, you diagnose the failure mode and generate targeted data to close the gap

• Communicates clearly: You distill technical progress into metrics that inform pricing and commercial decisions

• Stays hungry: You learn new frameworks, modalities, and customer domains quickly because you want to, not because you have to

• Fine-tune LFMs on customer data to hit quality and latency targets for on-device and edge deployments

• Generate and curate training data to address specific model failure modes

• Run experiments, track metrics, and iterate on solutions until customer success criteria are met

• Translate ambiguous customer requirements into concrete technical specifications

• Provide analytics to commercial teams for contract structuring and pricing

Must-have:

• Strong ML fundamentals: you understand how models learn, fail, and improve

• Hands-on experience with model customization, fine-tuning, or data generation

• Autonomous coding and debugging skills in Python

• Proficiency with open-source ML frameworks (Hugging Face, PyTorch)

Nice-to-have:

• Experience with vision or audio modalities

• Prior work at a startup or forward-deployed engineering role

• Contributions to open-source projects or an active Hugging Face profile

• Experience with deployment/inference frameworks like vLLM, SGLang, and llama.cpp

• You have owned technical delivery for at least one engagement that closed a contract

• Your work directly contributed to measurable B2B revenue

• Commercial teams have the metrics they need to price deals accurately because you provided them

• Real ML work: You will fine-tune models, generate data, and ship solutions, not just configure API calls

• 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