Member of Technical Staff - Post Training, Applied (Audio)

Liquid AI

San Francisco, CAHybridFull-timeNo compensation foundPosted 5mo agoChecked 1w ago

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At a glance

Compensation

No compensation found

Location

San Francisco, CAHybrid

Schedule

Full-time

Work Authorization

Not specified

Job overview

Liquid AI is hiring a Member of Technical Staff - Post Training, Applied (Audio). This role focuses on adapting Liquid AI's LFM2.5-Audio model for enterprise customers, specifically teaching audio models to understand user intents and translate them into structured tool calls. The position involves end-to-end ownership of applied post-training work, from data generation to delivery, and offers the opportunity to shape applied audio AI at a foundation model company.

Key focus areas include Act as the technical owner for enterprise audio post-training engagements., Translate customer requirements into concrete post-training specifications and workflows., and Design and build function calling capabilities for audio models..

Successful candidates bring Hands‑On Post‑Training Experience, Data Generation & Evaluation Pipelines, and Data Quality & Evaluation Design. Important skills include Post-Training For Language Models, SFT, Preference Alignment, RL, Data Generation, and Evaluation Pipelines For LLM Training. Preferred (not required): Speech Or Audio Language Models, Speech-to-Speech, ASR, and TTS.

Skills & qualifications

RequiredNice to have

Skills

Post-Training for Language ModelsSFTPreference AlignmentRLData GenerationEvaluation Pipelines for LLM TrainingEvaluation Pipelines for Audio Model TrainingData QualityEvaluation DesignFunction CallingTool UseStructured Output Training for Language ModelsOwnershipEnd-to-End ThinkingPragmatismThrives Under ConstraintsTechnical Owner for Enterprise Audio Post-Training EngagementsTranslate Customer Requirements Into Post-Training SpecificationsRun Supervised Fine-Tuning Workflows on Audio Language ModelsRun Reinforcement Learning Workflows on Audio Language ModelsSpeech or Audio Language ModelsSpeech-to-SpeechASRTTSMultimodal Audio-Text SystemsCustomer-Facing ML DeliveryApplied ML DeliveryAlignment Techniques Beyond Basic Supervised Fine-TuningOn-Device Inference ConstraintsLow-Latency Inference Constraints

Qualifications

Hands‑on Post‑Training ExperienceData Generation & Evaluation PipelinesData Quality & Evaluation DesignFunction Calling Familiarity

Benefits

Medical Insurance

Dental Insurance

Vision Insurance

401(k) Match

Paid Time Off

Full job description

ABOUT LIQUID AI

Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there.

THE OPPORTUNITY

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.

This role sits at the intersection of frontier audio models and real-world deployment. You'll own the applied post-training work that adapts LFM2.5-Audio for customer use cases end-to-end, from data generation through delivery. Unlike most roles that force a trade-off between customer impact and foundational work, this one gives you both: deep ownership over how audio models are adapted, evaluated, and shipped, and a direct line into the evolution of Liquid's post-training and audio stacks.

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.

WHAT WE’RE LOOKING FOR

We need someone who:

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.

THE WORK

Act as the technical owner for enterprise audio post-training engagements.
Translate customer requirements into concrete post-training specifications and workflows for LFM2.5-Audio and future audio models.
Design and build function calling capabilities for audio models: training models to map spoken user intents to structured tool calls (API invocations, parameter extraction, confirmation flows).
Design and execute data generation pipelines for speech-to-speech and text-to-text training, including synthetic dialogue, function calling examples, and intent-action pairs.
Run supervised fine-tuning, preference alignment, and reinforcement learning workflows on audio language models.
Design task-specific evaluations for audio function calling (intent recognition accuracy, parameter extraction, end-to-end task completion) and feed learnings back into core post-training pipelines.

DESIRED EXPERIENCE

Must-have:

Hands-on experience with post-training for language models (SFT, preference alignment, and/or RL).
Experience with data generation and evaluation pipelines for LLM or audio model training.
Strong intuition for data quality and evaluation design.
Familiarity with function calling, tool use, or structured output training for language models.

Nice-to-have:

Experience with speech or audio language models (speech-to-speech, ASR, TTS, or multimodal audio-text systems).
Prior exposure to customer-facing or applied ML delivery environments.
Experience with alignment or RL techniques beyond basic supervised fine-tuning.
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.

WHAT WE OFFER

Real ML work: You will fine-tune audio and speech models, build audio data pipelines, and ship solutions to enterprise customers under real-time on-device constraints.
Compensation: Competitive base salary with equity in a unicorn-stage company
Health: We pay 100% of medical, dental, and vision premiums for employees and dependents
Financial: 401(k) matching up to 4% of base pay
Time Off: Unlimited PTO plus company-wide Refill Days throughout the year

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