Machine Learning Applied Researcher - Speech, Vision and Audio

Job description

Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, smart people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same passion for innovation that goes into our products also applies to our practices strengthening our commitment to leave the world better than we found it. Join us to help deliver the next groundbreaking Apple product. Do you love working on challenges that no one has solved yet? As a member of our dynamic group, you will have the unique and rewarding opportunity to craft upcoming products that will delight and inspire millions of Apple’s customers every single day.
We are looking for a talented and experienced applied ML researcher who can both research new capabilities and develop frontier models using cutting-edge methods. In this role, you will take on high-risk, high-reward challenges - researching, designing, and improving state-of-the-art deep learning models trained on unique data, implementing novel machine learning algorithms, and developing solutions to problems that don't have obvious answers. Your scope will encompass various ML subfields and modalities, combining ideas from different fields to solve unique challenges and deliver solutions adopted broadly across teams.
<h3>Minimum Qualifications</h3>BS in Computer Science, Electrical Engineering, or a related field - or equivalent practical experience.
Experience in academic or industry research.
Experience working with Pytorch.
<h3>Preferred Qualifications</h3>MS or PhD in Computer Science, Electrical Engineering, or a related field with 10+ years of relevant industry experience.
Strong foundation in deep learning theory and hands-on experience training large-scale models.
Deep knowledge in one or more of: self-supervised learning, synthetic data generation, large language model training, or automatic speech recognition.
Experience working with multimodal data (e.g., images, audio, time-series, or sensor fusion).
Strong analytical and problem-solving skills; ability to translate research ideas into production-quality code.
Resilience and persistence - comfort with experimentation cycles where many attempts fail before one succeeds.
Publication record at top-tier ML venues (NeurIPS, ICML, ICLR, Interspeech, ICASSP, etc.).
Multidisciplinary background spanning fields such as neuroscience, physics, signal processing, or mathematics. Experience collaborating with distributed teams across time zones.