AIML - Staff ML Infrastructure Engineer, ML Platform & Technology - ML Compute
Apple
AIML - Staff ML Infrastructure Engineer, ML Platform & Technology - ML Compute
Job Description
Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something — you’ll add something! 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.
Locations
- San Francisco Bay Area, California, United States
Salary
Estimated Salary Rangemedium confidence
50,000,000 - 100,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Lead the development of infrastructure for large-scale workloadsintermediate
- Apache Sparkintermediate
- Rayintermediate
- distributed trainingintermediate
- Optimize platform efficiency and throughputintermediate
- resource managementintermediate
- Apache YuniKornintermediate
- Kueueintermediate
- Integrate features from distributed computing and ML frameworksintermediate
- Enhance scalability, performance, and observabilityintermediate
- improved monitoring and loggingintermediate
- Drive architectural evolutionintermediate
- adopt modern cloud-native technologiesintermediate
- improve system performance, efficiency, and scalabilityintermediate
- Reduce dev-ops effortsintermediate
- automating and streamlining operational processesintermediate
- Mentor engineersintermediate
- fostering skill growth and knowledge sharingintermediate
Required Qualifications
- Bachelors in Computer Science, engineering, or a related field (degree in computer science)
- 6+ years of hands-on experience in building scalable backend systems for training and evaluation of machine learning models (experience, 6 years)
- Proficient in relevant programming languages, like Python or Go (experience)
- Strong expertise in distributed systems, reliability and scalability, containerization, and cloud platforms (experience)
- Proficient in cloud computing infrastructure and tools: Kubernetes, Ray, PySpark (experience)
- Ability to clearly and concisely communicate technical and architectural problems, while working with partners to iteratively find solutions (experience)
Preferred Qualifications
- Advance degrees in Computer Science, engineering, or a related field. (degree in computer science)
- Hands-on experience with cloud-native resource management and scheduling tools like Apache YuniKorn. (experience)
- Experience with advanced architecture for distributed data processing and ML workloads. (experience)
- Proficient in working with and debugging accelerators, like: GPU, TPU, AWS Trainium. (experience)
Responsibilities
- 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.
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Answer 10 quick questions to check your fit for AIML - Staff ML Infrastructure Engineer, ML Platform & Technology - ML Compute @ Apple.
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AIML - Staff ML Infrastructure Engineer, ML Platform & Technology - ML Compute
Apple
AIML - Staff ML Infrastructure Engineer, ML Platform & Technology - ML Compute
Job Description
Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something — you’ll add something! 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.
Locations
- San Francisco Bay Area, California, United States
Salary
Estimated Salary Rangemedium confidence
50,000,000 - 100,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Lead the development of infrastructure for large-scale workloadsintermediate
- Apache Sparkintermediate
- Rayintermediate
- distributed trainingintermediate
- Optimize platform efficiency and throughputintermediate
- resource managementintermediate
- Apache YuniKornintermediate
- Kueueintermediate
- Integrate features from distributed computing and ML frameworksintermediate
- Enhance scalability, performance, and observabilityintermediate
- improved monitoring and loggingintermediate
- Drive architectural evolutionintermediate
- adopt modern cloud-native technologiesintermediate
- improve system performance, efficiency, and scalabilityintermediate
- Reduce dev-ops effortsintermediate
- automating and streamlining operational processesintermediate
- Mentor engineersintermediate
- fostering skill growth and knowledge sharingintermediate
Required Qualifications
- Bachelors in Computer Science, engineering, or a related field (degree in computer science)
- 6+ years of hands-on experience in building scalable backend systems for training and evaluation of machine learning models (experience, 6 years)
- Proficient in relevant programming languages, like Python or Go (experience)
- Strong expertise in distributed systems, reliability and scalability, containerization, and cloud platforms (experience)
- Proficient in cloud computing infrastructure and tools: Kubernetes, Ray, PySpark (experience)
- Ability to clearly and concisely communicate technical and architectural problems, while working with partners to iteratively find solutions (experience)
Preferred Qualifications
- Advance degrees in Computer Science, engineering, or a related field. (degree in computer science)
- Hands-on experience with cloud-native resource management and scheduling tools like Apache YuniKorn. (experience)
- Experience with advanced architecture for distributed data processing and ML workloads. (experience)
- Proficient in working with and debugging accelerators, like: GPU, TPU, AWS Trainium. (experience)
Responsibilities
- 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.
Tags & Categories
Answer 10 quick questions to check your fit for AIML - Staff ML Infrastructure Engineer, ML Platform & Technology - ML Compute @ Apple.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs