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

LFM2.5-Audio is Liquid's end-to-end multimodal speech and text language model. At 1.5B parameters, it handles speech-to-speech conversation, ASR, and TTS without requiring separate components, making it uniquely suited for real-time, on-device deployment. This role sits at the intersection of frontier audio models and real-world deployment. You'll own the applied post-training work that adapts LFM2.5-Audio for customer use cases end-to-end, from data generation through delivery.

Responsibilities

Act as the technical owner for enterprise audio post-training engagements; Translate customer requirements into concrete post-training specifications and workflows for LFM2.5-Audio and future audio models; Design and build function calling capabilities for audio models: training models to map spoken user intents to structured tool calls (API invocations, parameter extraction, confirmation flows); Design and execute data generation pipelines for speech-to-speech and text-to-text training, including synthetic dialogue, function calling examples, and intent-action pairs; Run supervised fine-tuning, preference alignment, and reinforcement learning workflows on audio language models; Design task-specific evaluations for audio function calling (intent recognition accuracy, parameter extraction, end-to-end task completion) and feed learnings back into core post-training pipelines

Requirements

Takes ownership; Thinks end-to-end; Is pragmatic; Thrives under constraints; Hands-on experience with post-training for language models (SFT, preference alignment, and/or RL); Experience with data generation and evaluation pipelines for LLM or audio model training; Strong intuition for data quality and evaluation design; Familiarity with function calling, tool use, or structured output training for language models

Nice to have

Experience with speech or audio language models (speech-to-speech, ASR, TTS, or multimodal audio-text systems); Prior exposure to customer-facing or applied ML delivery environments; Experience with alignment or RL techniques beyond basic supervised fine-tuning; Familiarity with on-device or low-latency inference constraints

Benefits

Real ML work: fine-tune audio and speech models, build audio data pipelines, and ship solutions to enterprise customers under real-time on-device constraints; Competitive base salary with equity in a unicorn-stage company; 100% paid 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 throughout the year