Member of Technical Staff - Multi-Modal, Audio

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

Our Audio team is building frontier speech-language models that handle STT, TTS, and speech-to-speech in a single architecture. This role sits at the center of applied audio model development, working directly with the technical lead to ship production systems that run on-device under real-time constraints. You will own critical workstreams across data pipelines, evaluation systems, and customer deployments. If you want high ownership on rare technical problems in a small, elite team where your code ships, this is the role.

What We're Looking For

We need someone who:

• Builds first, theorizes later: You ship working systems, not just notebooks. Production-grade code is your default, not a stretch goal.

• Owns outcomes end-to-end: From data pipelines to customer deployments, you take responsibility for the full stack without waiting for someone else to handle the hard parts.

• Thrives under constraints: On-device, low-latency, memory-limited systems excite you. You see constraints as design parameters, not blockers.

• Ramps quickly on new territory: Gaps in specific subdomains are fine if you close them fast. You seek out feedback and stay focused on what moves the needle.

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

Desired ExperienceMust-have:

• 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)