6 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
Liquid AI builds general-purpose AI systems that run efficiently from data center accelerators to on-device hardware, emphasizing low latency, memory efficiency, privacy, and reliability. The company partners with enterprises in consumer electronics, automotive, life sciences, and financial services to deploy and benchmark models for real-world workloads. Founded in 2023 out of MIT CSAIL and headquartered in Cambridge, Massachusetts, it is privately held.