About The Role

Boson AI builds real-time speech and multimodal AI that makes talking to AI feel like talking to people. Our Higgs models listen, speak, and see. Higgs Realtime is a speech-to-speech API with tool calling and support for 100+ languages, and it is drop-in compatible with the OpenAI Realtime API. Higgs TTS 3 is an expressive, open-weight text-to-speech model with zero-shot voice cloning. Higgs Avatar turns a single image into a streaming, talking video, and Higgs VoiceOver brings directable AI voice-over into video editors. We are a team of 40+ researchers and engineers, and we train our models on our own cluster of 900 GPUs.

Eligibility: this internship is only open to students enrolled in the Master of Science in Applied Computing (MScAC) program at the University of Toronto. Applications from anyone who is not an MScAC student at the University of Toronto will be automatically declined.

We are hiring MScAC students for the program's 8-month applied research internship, starting in May 2027; an earlier start is possible. You will join one of the projects below and work with a Boson AI mentor on problems that feed directly into our models and products. The internship is based in our Toronto office on a hybrid schedule.

Research Projects

• Audio data extraction and annotation. Our speech models learn from very large amounts of audio in 100+ languages. You will build pipelines that collect, segment, transcribe and annotate audio with as little manual engineering as possible, using unsupervised and semi-supervised methods, and you will close the loop so that annotation and model training improve each other (see NVIDIA Granary, arXiv:2505.13404). This suits you if you enjoy scripting, experiments and large datasets, and have a working knowledge of statistics and machine learning.
• Audio evaluation and benchmarking. Good speech is more than correct pronunciation. It has to fit the context, emotion and style, sound natural in a conversation, and match the voice it clones. You will design benchmarks and automatic metrics for expressive, multilingual and conversational speech, for voice cloning, and for speech recognition under the conditions real voice agents face (see our EmergentTTS-Eval, arXiv:2505.23009, and Back to Basics: Revisiting ASR in the Age of Voice Agents, arXiv:2603.25727), and use them both to evaluate our models and as signals for training them. This suits you if you like experimentation, have a good ear, ideally speak more than one language, and are comfortable automating evaluation in Python.