5 months ago
Responsibilities
- Own enterprise audio post-training engagements from requirements through delivery and evaluation.
- Translate customer requirements into post-training specifications and workflows for LFM2.5-Audio and future audio models.
- Design and build function-calling capabilities that map spoken intents to API invocations, extracted parameters, and confirmation flows.
- Design and execute data-generation pipelines for speech-to-speech and text-to-text training, including synthetic dialogue and intent-action pairs.
- Run supervised fine-tuning, preference-alignment, and reinforcement-learning workflows on audio language models.
- Design task-specific evaluations for intent recognition, parameter extraction, and end-to-end task completion.
- Feed applied learnings back into Liquid AI’s general-purpose post-training and audio pipelines.
Requirements
- Hands-on experience with language-model post-training, including supervised fine-tuning, preference alignment, and/or reinforcement learning.
- Experience building data-generation and evaluation pipelines for LLM or audio-model training.
- Strong judgment regarding data quality and evaluation design.
- Familiarity with function calling, tool use, or structured-output training for language models.
- Experience with speech or audio language models such as speech-to-speech, ASR, TTS, or multimodal audio-text systems is preferred.
- Experience in customer-facing or applied machine-learning delivery environments is preferred.
- Experience with alignment or reinforcement-learning techniques beyond basic supervised fine-tuning is preferred.
- Familiarity with on-device or low-latency inference constraints is preferred.
Benefits
- 100% of medical, dental, and vision premiums paid for employees and dependents
- 401(k) matching up to 4% of base pay
- Unlimited PTO and company-wide Refill Days
Categories
AI ResearchML Engineering
About Liquid AI
We build efficient general-purpose AI at every scale.