Member of Technical Staff - Post Training Applied Audio
Liquid AIVisit Liquid AI website
Liquid AI is an efficiency-first foundation-model company building device-native Liquid Foundation Models (LFMs) and tools to customize and deploy them.
Cambridge, Massachusetts, United States
Funding history
About Liquid AI
An MIT CSAIL spinout, Liquid AI develops general-purpose AI models focused on efficient deployment across CPUs, GPUs, NPUs, edge devices, and cloud or on-premises environments.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will own audio-model post-training projects from requirements through delivery. You will develop function-calling capabilities, create audio and text training-data pipelines, run fine-tuning and alignment workflows, evaluate task completion, and feed applied learnings into post-training pipelines.
Requirements
- Experience with language-model post-training using SFT, preference alignment, or reinforcement learning
- Experience with data-generation and evaluation pipelines for LLM or audio-model training
- Knowledge of data quality and evaluation design
- Familiarity with function calling, tool use, or structured-output training for language models
Responsibilities
- Own enterprise audio post-training engagements
- Translate customer requirements into post-training specifications and workflows
- Build function-calling capabilities that map spoken intents to structured tool calls
- Design data-generation pipelines for speech-to-speech and text-to-text training
- Run supervised fine-tuning, preference-alignment, and reinforcement-learning workflows on audio language models
- Design evaluations for audio function calling and feed learnings into post-training pipelines
Benefits
- Equity
- Medical, dental, and vision premiums fully paid for employees and dependents
- 401(k) matching up to 4% of base pay
- Unlimited PTO
- Company-wide Refill Days