Member of Technical Staff - Embedded ML Engineer (Audio/Omni)
AI summary of the role
This role leads the day-to-day model development pipeline for a marquee automotive partner engagement, translating ambiguous feature requests into production-ready model checkpoints for an on-device audio-to-function-calling model.
What you’ll do
- Own the core fine-tuning recipe for an on-device audio-to-function-calling model, ensuring tool calling accuracy across all supported languages.
- Generate, clean, and analyze training data; build and maintain large-scale data pipelines.
- Run training and evaluation cycles against partner requirements continuously through major software releases.
- Translate broad, ambiguous feature specs from partner product managers into concrete model training requirements.
What you’ll bring
- 2+ years hands-on machine learning experience training models end-to-end (computer vision, ADAS, LLMs, or audio).
- Experience with large-scale data pipelines and wrangling large volumes of data.
- Experience in automotive, embedded-device, or on-device ML context.
- Strong communication skills for client-facing interactions.
Technologies
fine-tuning · audio-to-function-calling · on-device ML · large-scale data pipelines · model training · embedded ML · tool-use fine-tuning · multilingual models
About Liquid AI
Efficient general-purpose AI systems optimized for on-device deployment across data centers and edge hardware, enabling low-latency, privacy-preserving enterprise AI.
Series A
Employer postings · Data from · Sources