Applied Machine Learning Scientist
Santa Clara Valley (Cupertino), California, United States
Imagine what you could do as an applied machine learning scientist here. At Apple, novel machine learning ideas have a way of becoming phenomenal products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Here is your opportunity to be part of an incredible research and engineering team building the next-generation of advanced algorithms for sensing technologies in iPhone, Apple Watch, iPad and more. We are looking for smart, creative applied machine learning scientists with expertise in deep learning algorithms for image or time-series data. Working knowledge of signal processing, probabilistic modeling, statistics and embedded programming will broaden your role and effectiveness in this position.
- Strong background in Deep Learning and classical Machine Learning
- Experience with one or more Deep Learning packages including but not limited to TensorFlow and PyTorch.
- Track record of coming up with new ML ideas, as proven by publications, patents or open-source projects.
- Past experience in creating high-performance implementations of deep learning algorithms
- Experience with Scikit-learn
- Strong programming skills in one or more general purpose programming languages, including but not limited to: Python or C/C++
We're looking for smart and creative engineers and scientists with a strong background in deep learning. Your passion for leading state-of-the-art sensing technologies will be essential to your project and a key component to our team of world-class engineers and designers who are driven to create the next big thing using deep learning. Join us to enhance the lives of millions of users. You will contribute to advanced algorithms that transform raw image or time-series data into interpretable information that feed into elegant applications that delight, connect, and encourage Apple users all around the world. The emphasis will be to work on edge of creativity technologies, but highly impactful deep learning problems that will help improve our users' experience, which has always been at the core of Apple's agenda. More specifically, you will: Design and implement machine learning algorithms that process image or time-series data measured by various sensors in different Apple products Deploy Apple's massive computing platform with thousands of GPU's and CPU's ready just for you to establish scalable, efficient, automated processes for large-scale data analyses, model development and model validation. Communicate advances and ideas to a focused team - Develop innovative tools and metrics that change the way we look at problems. Join a thriving Machine Learning community at Apple (https://machinelearning.apple.com) Work cross-functionally with sensor architects and software engineers to build the next generation of sensing technologies.
Education & Experience
Ph.D. degree in CS (preferred), or other STEM fields such as EE, or Statistics. M.S. in CS with at least 2 years of experience in research and development of deep learning algorithms. Apple is a diverse collective of thinkers and doers, continually reimagining what’s possible to help us all do what we love in new ways. The people who work here have reinvented entire industries with the Mac, iPhone, iPad, and Apple Watch, as well as with services, including iTunes, the App Store, Apple Music, and Apple Pay. And 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. Apple is an equal opportunity employer that is committed to inclusion and diversity. Visitjobs.apple.comto learn more.
- Experience processing large-scale data sets using Mesos, Spark, or Hadoop
- Experience with signal processing
- Experience in deep learning algorithms for automated speech recognition, or computer vision
- Familiar with state-of-the-art Generative models (e.g. GANs, VAEs) for challenging problems with multiple modalities of data. Deep sequential models (e.g. RNNs) for understanding very rich sequence data. Semi-supervised, unsupervised learning using Deep Neural nets.
- Experience with embedded programming