Liquid AI

1 day ago

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

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 28 minutes agoStatus: Live

Job Summary

Build and own an end-to-end on-device ML model development pipeline for a marquee automotive partner. Translate ambiguous feature requests into concrete fine-tuning requirements, generate and wrangle large-scale training data, and run continuous training and evaluation cycles through major software releases. Deliver production-ready model checkpoints with clear dashboards, analyses, and documentation, while maintaining reliable audio-to-function-calling tool use across supported languages.

Location: San Francisco

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Translate ambiguous partner feature specs into concrete model training requirements.
•Own the fine-tuning recipe for an on-device audio-to-function-calling model and ensure accurate, reliable tool calling across supported languages.
•Generate, clean, analyze training data and build/maintain large-scale data pipelines that feed training.
•Run training and evaluation cycles against partner requirements continuously through major software releases.
•Create polished, partner-facing deliverables including dashboards, analyses, documentation, and model checkpoints.

Pay and Benefits

Equity and Bonus:Equity

Perks:Health InsuranceDentalVision401kPaid Leave

Key Requirements

•Hands-on machine learning experience (roughly 2+ years; open to exceptional early-career candidates with strong internship track records).
•Personally trained models end-to-end across modalities (computer vision, ADAS, LLMs, audio); building apps around model APIs does not qualify.
•Experience with large-scale data pipelines and wrangling large volumes of data.
•Experience in automotive, embedded-device, or on-device ML contexts with first-principles reasoning about those environments.
•Strong communication skills for a client-facing role.

Experience:2+ yearsAutomotiveEmbedded-device MLOn-device MLMachine learning

Skills:CommunicationClient-ready communicationFirst-principles reasoningData wranglingOwnership

Company Brief

Liquid AI

Builds AI infrastructure and tooling to enable real-time, distributed machine learning and orchestration across edge and cloud environments, simplifying deployment and management of intelligent applications.

Industry: AI & Machine Learning

Website

Loading job details...

Preparing the role view and application actions.

Liquid AI

1 day ago

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

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 28 minutes agoStatus: Live

Job Summary

Location: San Francisco

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Translate ambiguous partner feature specs into concrete model training requirements.
•Own the fine-tuning recipe for an on-device audio-to-function-calling model and ensure accurate, reliable tool calling across supported languages.
•Generate, clean, analyze training data and build/maintain large-scale data pipelines that feed training.
•Run training and evaluation cycles against partner requirements continuously through major software releases.
•Create polished, partner-facing deliverables including dashboards, analyses, documentation, and model checkpoints.

Pay and Benefits

Equity and Bonus:Equity

Perks:Health InsuranceDentalVision401kPaid Leave

Key Requirements

•Hands-on machine learning experience (roughly 2+ years; open to exceptional early-career candidates with strong internship track records).
•Personally trained models end-to-end across modalities (computer vision, ADAS, LLMs, audio); building apps around model APIs does not qualify.
•Experience with large-scale data pipelines and wrangling large volumes of data.
•Experience in automotive, embedded-device, or on-device ML contexts with first-principles reasoning about those environments.
•Strong communication skills for a client-facing role.

Experience:2+ yearsAutomotiveEmbedded-device MLOn-device MLMachine learning

Skills:CommunicationClient-ready communicationFirst-principles reasoningData wranglingOwnership

Company Brief

Liquid AI

Industry: AI & Machine Learning

Website