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Description
As a staff engineer on ML Compute team, your work will include:
- Lead the development of the infrastructure to run large-scale workloads on the Cloud, such as Apache Spark, Ray, and distributed training.
- Optimize platform efficiency and throughput by improving resource management capabilities with schedulers like Apache YuniKorn and Kueue.
- Integrate new features from core distributed computing and ML frameworks into the platform, offering them to production users and providing support.
- Enhance the platform's scalability, performance, and observability through improved monitoring and logging.
- Drive the architectural evolution of the platform by adopting modern, cloud-native technologies to improve system performance, efficiency, and scalability.
- Reduce dev-ops efforts by automating and streamlining operational processes.
- Mentor engineers in areas of your expertise, fostering skill growth and knowledge sharing.
Minimum Qualifications
Bachelors in Computer Science, engineering, or a related field
6+ years of hands-on experience in building scalable backend systems for training and evaluation of machine learning models
Proficient in relevant programming languages, like Python or Go
Strong expertise in distributed systems, reliability and scalability, containerization, and cloud platforms
Proficient in cloud computing infrastructure and tools: Kubernetes, Ray, PySpark
Ability to clearly and concisely communicate technical and architectural problems, while working with partners to iteratively find solutions
Preferred Qualifications
Advance degrees in Computer Science, engineering, or a related field.
Hands-on experience with cloud-native resource management and scheduling tools like Apache YuniKorn.
Experience with advanced architecture for distributed data processing and ML workloads.
Proficient in working with and debugging accelerators, like: GPU, TPU, AWS Trainium.
Eeo Content
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.
