Member of Technical Staff - Post Training, Applied
Remote • San Francisco • FullTime
Posted 8mo ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
Applied AI Engineer
About the job
Liquid AI, spun out of MIT CSAIL, builds general-purpose AI systems that run efficiently across various deployment targets. We partner with enterprises in consumer electronics, automotive, life sciences, and financial services. This role offers a unique opportunity to lead applied post-training work end-to-end for text workloads, adapting Liquid Foundation Models for major enterprise clients. You will serve as the technical liaison between customer needs and model delivery, owning engagements from scoping to evaluation. Between projects, you will develop reusable applied workflows and tooling to enhance future delivery. This position is ideal for someone passionate about data quality, evaluation design, and making language models effective in production environments.
Responsibilities
- 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.
Requirements
- 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 like chat model alignment, instruction tuning, or text data curation at scale.
- Proficiency with the 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.
Benefits
- Competitive base salary with equity in a unicorn-stage company.
- 100% coverage of medical, dental, and vision premiums for employees and dependents.
- 401(k) matching up to 4% of base pay.
- Unlimited PTO plus company-wide Refill Days.