Requirements

M.S. or PhD in Computer Science or a related field.
Experience with machine learning for speech recognition, machine translation, NLP or speech synthesis.
Hands-on experience with deep learning toolkits such as PyTorch, JAX or TensorFlow.

Nice to Haves

Research or product experience in speech-to-speech or speech-to-text translation, especially end-to-end, streaming or simultaneous approaches.
Experience in text-to-speech and speech generation, such as neural TTS, speech tokenizers and audio codecs, expressive or multilingual synthesis.
Experience with large language models or multimodal (audio + text) foundation models, including pre-training, fine-tuning or reinforcement learning.
Experience designing and deploying real-world, large-scale, user-facing speech recognition, translation or synthesis systems, including on-device or low-latency inference.
Familiarity with speech and translation evaluation, such as WER, BLEU/chrF/COMET-style metrics, MOS and side-by-side human evaluation, and latency metrics.
Ability to formulate a research problem, then design, run experiments and implement solutions in Python and C/C++.
Publications in relevant venues (e.g. Interspeech, ICASSP, ACL, EMNLP, NeurIPS, ICML) are a plus.
Excellent spoken and written communication skills.

Have a significant impact by shipping speech-to-speech translation technology to millions of real-world users.
Enable users to communicate seamlessly across languages, in conversation, on calls and through their devices.
Address the practical problems of real-time translation at scale: translation quality, latency and streaming, natural and expressive speech output, robustness to real-world audio, and evaluation that reflects the user experience.
Collaborate with researchers, engineers and designers to put speech-to-speech translation technology in the hands of millions of customers.