Embedded ML Engineer – Audio/Omni (Member of Technical Staff)

About the role

Liquid AI is looking for an Embedded Machine Learning Engineer to own the end‑to‑end model development pipeline for a high‑impact automotive partnership. You will translate ambiguous product specifications into production‑ready audio‑to‑function‑calling models that run efficiently on on‑device hardware.

Key responsibilities

• Collaborate with partner product managers to turn broad feature requests into concrete model training requirements.
• Own the fine‑tuning recipe for an on‑device audio‑to‑function‑calling model, ensuring accuracy across multiple languages.
• Generate, clean, and analyze large‑scale training data; build and maintain data pipelines that feed model training.
• Run continuous training and evaluation cycles aligned with partner release schedules.
• Produce clear dashboards, analyses, documentation, and polished deliverables for partners.
• Progressively take full ownership of the model development pipeline from spec intake to checkpoint delivery.

Required profile

• Self‑starter who can drive loosely defined tasks to completion without step‑by‑step direction.
• Excellent communicator able to explain technical work clearly to non‑technical stakeholders.
• High energy and urgency; comfortable shipping against real deadlines and handling crunch periods.
• Low ego; willing to perform unglamorous work such as data cleaning, documentation, and dashboard creation.
• Adaptable to shifting requirements and evolving partner specifications.

Required skills

• Hands‑on machine learning experience (2+ years or strong internship track record).
• Experience training models end‑to‑end in any modality (e.g., audio, vision, LLMs).
• Building and maintaining large‑scale data pipelines for training data.
• Model fine‑tuning and evaluation for on‑device deployment.