Member of Technical Staff - Post Training, Applied (Text)

Liquid AI; San Francisco (CA); On-site; USD 100,000 - 140,000; Full time

Benefits offered by this job

Competitive base salary with equity

100% paid health, dental, and vision premiums

401(k) matching up to 4%

Unlimited PTO plus Refill Days

Job summary

A cutting-edge AI company in Boston is looking for a dedicated professional to own applied post-training projects for enterprise clients. You will work hands-on with data generation and evaluation, translating customer needs into actionable specifications. Ideal candidates have strong experience in model fine-tuning and a solid understanding of data quality. The role offers competitive salary and extensive benefits, including 100% paid health premiums and unlimited PTO.

Qualifications

• Hands-on experience with data generation and evaluation for LLM post-training.
• Experience training or fine-tuning models using SFT, preference alignment, or reinforcement learning.
• Strong intuition for data quality and evaluation design.

Responsibilities

• Act as the technical owner for enterprise customer post-training engagements.
• Translate customer requirements into concrete post-training specifications.
• Design and execute data generation, filtering, and quality assessment processes.
• Run supervised fine-tuning and preference alignment workflows.
• Interpret results and feed learnings back into core post-training pipelines.

Skills

Data generation

Evaluation design

Model fine-tuning

Clear communication

Job description

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.

The Opportunity

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.

What We're Looking For

We need someone who:

• Takes ownership: Owns post-training projects end-to-end, from customer requirements through delivery and evaluation.

• Thinks end-to-end: Can reason across data generation, training, alignment, and evaluation as a single system.

• Is pragmatic: Optimises for model quality and customer outcomes over publications or theory.

• Communicates clearly: Can translate between customer needs and internal technical teams, and push back when needed.
  

The Work

• 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.

Desired Experience

Must-have:

• 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 Success Looks Like (Year One)

• Independently owns and delivers enterprise post-training projects with minimal oversight.

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

• Has made durable contributions to Liquid’s general-purpose post-training pipelines by feeding applied learnings back into baseline model development.

What We Offer

• 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