Member of Technical Staff - Post Training, Applied

About the role

Liquid AI builds general-purpose AI systems that run efficiently across various deployment targets, including data center accelerators and on-device hardware. We collaborate with enterprises in sectors such as consumer electronics, automotive, life sciences, and financial services. We are expanding rapidly and seek talented individuals to contribute to our growth.

Opportunity

This role offers an opportunity to lead applied post-training work for text workloads, adapting Liquid Foundation Models for large enterprise customers. You will bridge customer requirements with model delivery, managing projects from scoping through evaluation and building reusable workflows for future engagements.

What We're Looking For

• Takes ownership: Owning customer post-training projects end-to-end, from requirements through delivery and evaluation.
• Thinks end-to-end: Reasoning across data generation, instruction tuning, alignment, and evaluation as a cohesive system.
• Is pragmatic: Prioritizing model quality and customer outcomes over publications or theoretical advancements.
• Communicates clearly: Translating between customer needs and internal technical teams, and pushing back when necessary.

The Work

• Act as the technical owner for enterprise customer post-training engagements involving text workloads.
• Translate customer requirements into concrete post-training specifications and workflows.
• Design and execute data generation, filtering, and quality assessment processes for text corpora.
• Run supervised fine-tuning, instruction tuning, RLHF, DPO, and other preference alignment workflows.
• Design task-specific evaluations for text model performance and interpret results.
• Build reusable applied tooling and workflows that accelerate future customer engagements.

Must-have Experience

• 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.

Desired Experience

• 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.

Success Looks Like (Year One)

• Independently owns and delivers enterprise post-training projects for text workloads with minimal oversight.
• Trusted by customers as the technical owner, demonstrating strong judgment and delivery quality.
• Built reusable applied workflows or tooling that accelerate future customer engagements.

What We Offer

• Real ML work: Fine-tuning models, generating data, and shipping solutions, directly impacting core model development.
• Compensation: Competitive base salary with equity in a unicorn-stage company.
• Health: 100% coverage 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.