Apple is looking for Machine Learning Engineers to enable personalized Siri interactions and deliver this technology to users globally, pushing the envelope on the latest research developments in speaker recognition and deploying on-device models that align with Apple's core values of quality, innovation, and user privacy.
Requirements
- 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)
Responsibilities
- Build end-to-end model training and evaluation pipelines.
- Push the envelope on the latest research developments in speaker recognition.
- Deploy machine-learned, on-device models that are aligned with the core values of Apple, ensuring the highest standards of quality, innovation, and respect for user privacy.
- Build and deploying models that constantly advance the state-of-the-art.
- Measure the impact of our deployed models not just on pre-ship evaluation sets, but also post-ship on production traffic.
- Optimize error rates on existing data.
- 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.
Other
- 5+ years of post-baccalaureate or equivalent experience
- 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
