Build data pipelines, evaluation systems, and customer fine-tuning workflows for frontier speech-language models that handle STT, TTS, and speech-to-speech in one architecture. Contribute production code to the core audio repository and ship models that run on-device under real-time constraints. Requires PyTorch and experience shipping production ML systems; distributed training frameworks like DeepSpeed or FSDP are also required.
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:
Must-have:
Nice-to-have: