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

Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there.

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.

We're now bringing this model to enterprise customers. The core challenge: teaching audio models to understand user intents and translate them into structured tool calls. Think voice-driven function calling, where a spoken request triggers the right API, extracts the right parameters, and confirms back to the user in natural speech.

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. Unlike most roles that force a trade-off between customer impact and foundational work, this one gives you both: deep ownership over how audio models are adapted, evaluated, and shipped, and a direct line into the evolution of Liquid's post-training and audio stacks.

• 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.

• 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.

• Independently owns and delivers enterprise audio post-training projects with minimal oversight.

• Has built and shipped function calling capabilities that reliably translate spoken user intents into tool calls for production use cases.

• Is trusted by customers as the technical owner, demonstrating strong judgment and delivery quality.

• Has made durable contributions to Liquid's general-purpose post-training and audio pipelines by feeding applied learnings back into baseline model development.