Internship - Machine Learning Research - Speech-to-Speech Translation

**Summary** The Machine Translation and Multilingual AI team is looking for research interns to explore open problems in speech-to-speech translation: how to build models that let people talk naturally with anyone, in any language, in real time. **Description** Speech-to-speech translation is moving from cascaded pipelines to end-to-end models that listen, translate and speak in a single system. Many core questions remain open. How do you translate simultaneously, before the speaker has finished? How do you keep translated speech natural and expressive? How do large multimodal models best combine speech understanding, translation and speech generation? And how should these systems be evaluated? As an intern, you will own a focused research project on one of these questions, working closely with researchers and engineers on the team. You will have access to large-scale data and compute, and we encourage publishing results at top venues. **Example Research Directions Include** * Streaming and simultaneous speech translation: policies for when to listen and when to speak, and the trade-off between quality and latency. * Speech generation for translation: natural, expressive and prosodically appropriate output, including speech tokenizers and audio codecs. * Multimodal foundation models for speech: how pre-training, fine-tuning and reinforcement learning shape speech-to-speech ability across languages. * Multilingual scaling: extending to new languages and handling code-switching and unknown source languages. * Evaluation: automatic metrics and human evaluation methods that reflect real conversational experience. **Responsibilities** * Define a research question with your mentor, review related work, and propose an approach. * Design and run experiments on large-scale speech and translation models. * Analyse results rigorously and share findings with the team. * Write up your work, with the goal of a publication or a reusable research contribution. **Minimum Qualifications** * Currently enrolled in a PhD or Master's program in Computer Science, Electrical Engineering, Linguistics or a related field. * Research experience in at least one of: speech recognition, speech synthesis, machine translation, NLP or multimodal learning. * Hands-on experience with deep learning toolkits such as PyTorch, JAX or TensorFlow. * Strong programming skills in Python. **Preferred Qualifications** * Research experience in speech-to-speech or speech-to-text translation, especially end-to-end, streaming or simultaneous approaches. * Research experience in text-to-speech or speech generation, such as neural TTS, speech tokenizers, audio codecs, or expressive and multilingual synthesis. * Experience training or fine-tuning large language models or multimodal (audio + text) models. * Familiarity with speech and translation evaluation, such as WER, BLEU/chrF/COMET-style metrics, MOS and human preference studies, and latency metrics. * Publications at relevant venues (e.g. Interspeech, ICASSP, ACL, EMNLP, NeurIPS, ICML, ICLR). * Excellent spoken and written communication skills. At Apple, we're not all the same. And that's our greatest strength. We draw on the differences in who we are, what we've experienced and how we think. Because to create products that serve everyone, we believe in including everyone. Therefore, we are committed to treating all applicants fairly and equally. As a registered Disability Confident employer, we will work with applicants to make any reasonable accommodations. Apple will consider for employment all qualified applicants with criminal backgrounds in a manner consistent with applicable law. Learn more At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong. Learn about accessibility in Apple’s workplace Role Number: 200690125-1251