Apple is looking for Machine Learning Engineers to develop and advance frictionless voice invocation experiences on Apple devices, enabling new conversational features for Siri interactions. The role involves building and deploying models that constantly advance the state-of-the-art, optimizing error rates, defining new user experience metrics, and innovating within the constraints of on-device computation.
Requirements
- 3-5 years of experience with scalable machine learning technologies
- Strong background in machine learning and deep learning; experience in speech recognition is highly desired
- Proficiency in deep learning / machine learning frameworks (e.g., PyTorch, TensorFlow) and programming languages including but not limited to C/C++/Python, with strong software engineering fundamentals and an interest in optimizing, automating, and scaling end-to-end systems (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
- Outstanding problem solving, critical thinking, creativity, and interpersonal skills
- Industry experience in product development and deployment and understanding of full software product life cycle
Responsibilities
- Build end-to-end model training and evaluation pipelines.
- 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.
- Develop and advance frictionless voice invocation experiences.
- Be responsible for developing and integrating Siri’s speech and audio experience in a full range of Apple devices.
- Collaborate with researchers to develop advanced machine learning (ML) technologies.
- Focus on improving the ML training and evaluation infrastructure for improved research efficiency, and faster modeling iterations.
- Develop agile deployment processes which are easier to scale using the best automation practices.
Other
- ability to communicate effectively and to work well in multi-functional teams
- Master’s or Ph.D. degree in Computer Science, Electrical Engineering or related field; outstanding candidates with Bachelor’s degrees and multiple years of significant engineering/product experience will also be considered
