Embedded ML Engineer: On-Device Audio & Model Pipeline Lead

Liquid AI; United States; On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Health premiums covered

401(k) matching

Unlimited PTO

Job summary

Liquid AI, a MIT CSAIL spin-out, seeks a machine learning engineer to own the end-to-end model development pipeline for a marquee automotive engagement. You will translate ambiguous feature specs into training requirements, generate and clean data, train and evaluate checkpoints, and deliver production-ready models for on-device use.

You will work closely with an embedded engineer, drive fast-moving development under real deadlines, and communicate complex concepts to partners with clarity.

Qualifications

• Hands-on ML experience: ~2+ years, strong internship track accepted.
• End-to-end model training across modalities (vision, ADAS, LLMs, audio).
• Experience with large-scale data pipelines and wrangling big datasets.
• Experience in automotive/embedded/on-device ML contexts and reasoning from first principles.
• Strong communication skills; this is a client-facing role.

Responsibilities

• Join partner calls and translate broad feature specs into concrete training requirements.
• Own the core fine-tuning recipe for on-device audio-to-function-calling model across languages.
• Generate, clean, and analyze training data; build and maintain large-scale data pipelines.
• Run training and evaluation cycles against partner requirements through software releases.
• Make fast-moving work presentable with dashboards, analyses, and partner-facing deliverables.
• Progressively own the end-to-end model development pipeline from spec intake to delivered checkpoint.

Skills

Hands-on ML experience

End-to-end model training

Large-scale data pipelines

Strong communication

Client-facing

Job description

Liquid AI, a MIT CSAIL spin-out, seeks a machine learning engineer to own the end-to-end model development pipeline for a marquee automotive engagement. You will translate ambiguous feature specs into training requirements, generate and clean data, train and evaluate checkpoints, and deliver production-ready models for on-device use.

You will work closely with an embedded engineer, drive fast-moving development under real deadlines, and communicate complex concepts to partners with clarity.