Hiring: Member of Technical Staff - Post Training, Applied (Audio) Company: Liquid AI Location: San Francisco, CA Job Posted Time: 2026-09-03 10:30:51 Target Skills & Keywords : Data Pipeline, Fine-tuning, LLM, Reinforcement Learning About the job Experience: •Applied hands-on capability in post-training for language models (SFT, preference alignment, and/or RL). •Strong intuition for data quality and evaluation design. •Operational familiarity with function calling, tool use, or structured output training for language models. Required Skills: •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. •If you care about data quality, evaluation, and making models actually work in production, this is a chance to shape how applied audio AI is done at a foundation model company. •Takes ownership: Owns customer post-training projects end-to-end for audio workloads, from requirements through delivery and evaluation. •Thinks end-to-end: Can reason across audio data pipelines, speech-text alignment, model adaptation, and evaluation as a connected system. •Is pragmatic: Optimizes for model quality and customer outcomes over publications or theory. •Thrives under constraints: On-device, low-latency, memory-limited audio systems excite you. You see constraints as design parameters, not blockers. •Act as the technical owner for enterprise audio post-training engagements. Qualifications: •Prior exposure to customer-facing or applied ML delivery environments. •Operational familiarity with on-device or low-latency inference constraints. •What Success Looks Like (Year One) •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. Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!