Member of Technical Staff - Multi-Modal - Audio

About the role

Liquid AI builds general-purpose AI systems that run efficiently across various deployment targets, including data center accelerators and on-device hardware. We collaborate with enterprises in sectors such as consumer electronics, automotive, life sciences, and financial services. We are currently scaling rapidly and are seeking talented individuals to join our team.

Opportunity

The Audio team at Liquid AI is developing cutting-edge speech-language models that handle speech-to-text (STT), text-to-speech (TTS), and speech-to-speech tasks in a unified framework. This role involves working closely with the technical lead to develop production systems that can run on-device under real-time constraints. You will be responsible for critical workstreams across data pipelines, evaluation systems, and customer deployments.

What We're Looking For

• Builds first, theorizes later: Ship working systems, not just notebooks.
• Owns outcomes end-to-end: From data pipelines to customer deployments, you take full responsibility for the stack without waiting for others to handle the hard parts.
• Thrives under constraints: Excited about on-device, low-latency, memory-limited systems; sees constraints as design parameters rather than roadblocks.
• Ramps quickly on new territory: Can quickly fill gaps in specific subdomains and stays focused on what drives progress.

The Work

• Build and scale data pipelines for audio model training, including preprocessing, augmentation, and quality filtering at scale.
• Design, implement, and maintain evaluation systems that measure multimodal performance across internal and public benchmarks.
• Fine-tune and adapt audio models for customer-specific use cases, owning delivery from requirements through deployment.
• Contribute production code to the core audio repository, collaborating with infrastructure and research teams.
• Support experimentation under real hardware constraints, shifting between customer work and core development as priorities evolve.

Must-have Experience

• Strong programming fundamentals with demonstrated ability to write clean, maintainable, production-grade code.
• Experience building and shipping production ML systems beyond model training (data pipelines, evals, serving infrastructure).
• Proficiency in PyTorch and familiarity with distributed training frameworks (DeepSpeed, FSDP, or similar).
• Track record of collaborating effectively in shared codebases with high engineering standards.

Desired Experience

• Direct experience with audio/speech models (ASR, TTS, vocoders, diarization, or speech-to-speech systems).
• Experience designing and running large-scale training experiments on distributed GPU clusters.
• Open-source contributions that demonstrate code quality and engineering judgment.

What Success Looks Like (Year One)

• Within 6 months, independently deliver production-ready data pipelines or evaluation systems and own at least one customer workstream end-to-end.
• Your PRs to the core audio repo are accepted without heavy rework, demonstrating strong judgment in system design.
• By year end, operate as a second pillar to the technical lead, unblocking parallel workstreams and raising overall team velocity.

What We Offer

• Rare technical problems: Work on audio-to-audio frontier systems with real ownership in a small, elite team.
• Compensation: Competitive base salary with equity in a unicorn-stage company.
• Health: We cover 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.