Machine Learning Engineer, Siri Attention & Invocation
Apple
Full-time
On-site
Seattle, Washington, United States
Machine Learning
You will be part of a team whose focus is on applied machine learning, on building and deploying models that constantly advance the state-of-the-art. But that is only half the story! In Siri Attention & Invocation, we own our user journeys end-to-end. We measure the impact of our deployed models not just on pre-ship evaluation sets, but also post-ship on production traffic. We optimize error rates on existing data. We also define new metrics that take into account the user experience we want to deliver and apply them to the data that best represents the next feature we ship. And we are sometimes constrained by the limits of on-device computation — that is where your ability to innovate will be most impactful.
You will collaborate with many dynamic, cross-functional teams consisting of software engineers and machine learning engineers/scientists. The ideal candidate will excel in both academic rigor and engineering efficacy, staying up-to-date with the latest research advancements as well as delivering reliable and robust models to all devices for all users around the world. If you are passionate about building outstanding products and using the full spectrum of your skills to extend the core technology that lets Siri understand, personalize, and interact in new and exciting ways, then we cannot wait to hear from you.
- 5+ years of post-baccalaureate or equivalent experience with the following:
- Strong background in machine learning and deep learning; experience in speech, speaker, and/or language recognition a plus, but not required
- Solid foundation in machine learning fundamentals, such as classification, feature engineering, clustering, semi-supervised learning, and domain adaptation
- Proficiency in deep learning / machine learning frameworks (e.g., PyTorch, TensorFlow) and scripting languages (e.g., Python, bash), with strong software engineering fundamentals and an interest in optimizing, automating, and scaling end-to-end systems globally (e.g., PySpark, Airflow)
- Strong attention to detail, along with the analytical skills and the willingness to dive into data to explain anomalies and conduct error/deviation analyses (e.g., Jupyter)
- Outstanding problem solving, critical thinking, creativity, and interpersonal skills; ability to communicate effectively with engineers, scientists, managers, and cross-functional partners
- Master’s or Ph.D. degree in electrical engineering, computer science, machine learning, language technology, or related fields; outstanding candidates with Bachelor’s degrees and multiple years of significant engineering/product experience will also be considered