Member of Technical Staff - Multi-Modal, Audio

About the role

Spun out of MIT CSAIL, Liquid AI builds 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. The 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.

Responsibilities

• 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

Requirements

Must-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)
• 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

Nice-to-have

• 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

• Within 6 months, you 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, you operate as a second pillar to the technical lead, unblocking parallel workstreams and raising overall team velocity

Benefits

• Rare technical problems: Work on audio-to-audio frontier systems with real ownership in a team small enough that your contributions ship directly to production
• Competitive base salary with equity in a unicorn-stage company
• We pay 100% of medical, dental, and vision premiums for employees and dependents
• 401(k) matching up to 4% of base pay
• Unlimited PTO plus company-wide Refill Days throughout the year