Member of Technical Staff - Multi-Modal, Audio

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.

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.

We need someone who:

• →

  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

• 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

• 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