AIML - Staff Machine Learning Engineer - ML Efficiency, ML Platform & Technology
Apple
AIML - Staff Machine Learning Engineer - ML Efficiency, ML Platform & Technology
Job Description
Do you want to shape the platform that enables the next generation of intelligent experiences on Apple products & services? In Apple’s Machine Learning Platform Technology & Infra team we have built the platform that Apple uses for developing machine learning, artificial intelligence, and computer vision applications. As a team, we have a variety of technical backgrounds, from machine learning PhDs to builders of large-scale production systems. Specifically in this role you will be working on optimizing end-to-end system performance of distributed machine learning workloads. This is a highly collaborative role and you will be working with key partners across the company. We are seeking highly motivated and experienced engineers to join our team. The ideal candidate will have a deep understanding of machine learning systems and cloud computing infrastructure. Key responsibilities in this role are: - Engage with ML researchers to optimize end-to-end performance of large scale distributed ML workloads - Analyze workload metrics to identify sources of inefficiencies and work with users to understand and optimize ML workloads - Conduct workload analysis based on benchmarking key workloads on deployed systems - Improve large scale training resiliency by optimizing applications and frameworks for improved recovery from failures and preemptions - Influence architecture, design, development, and operations of next generation ML accelerator systems based on workload insights
Locations
- Santa Clara, California, United States 95050
- Seattle, Washington, United States 98117
Salary
Estimated Salary Rangemedium confidence
8,000,000 - 15,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- machine learningintermediate
- artificial intelligenceintermediate
- computer visionintermediate
- distributed machine learningintermediate
- system performance optimizationintermediate
- cloud computing infrastructureintermediate
- workload analysisintermediate
- benchmarkingintermediate
- large scale training resiliencyintermediate
- ML accelerator systems architectureintermediate
- ML accelerator systems designintermediate
- ML accelerator systems developmentintermediate
- ML accelerator systems operationsintermediate
- collaborationintermediate
Required Qualifications
- Experience working with large scale parallel and distributed accelerator-based systems (experience)
- Experience optimizing performance and AI workloads at scale (experience)
- Experience developing code in one or more of training frameworks (such as PyTorch, TensorFlow or JAX) (experience)
- Strong communicator with ability to analyze complex and ambiguous problems (experience)
- Programming and software design skills (proficiency in C/C++ and/or Python) (experience)
- Experience working in a high-level collaborative environment and promoting a teamwork mentality (experience)
- Bachelor's degree in Computer Science and 7+ years of work experience (experience, 7 years)
Preferred Qualifications
- Deep understanding of computer systems and the interactions between HW and SW (experience)
- Experience in performance analysis and optimization experience in Cloud accelerators (experience)
- Advanced degree in CS (degree in cs)
Responsibilities
- We are seeking highly motivated and experienced engineers to join our team. The ideal candidate will have a deep understanding of machine learning systems and cloud computing infrastructure. Key responsibilities in this role are: - Engage with ML researchers to optimize end-to-end performance of large scale distributed ML workloads - Analyze workload metrics to identify sources of inefficiencies and work with users to understand and optimize ML workloads - Conduct workload analysis based on benchmarking key workloads on deployed systems - Improve large scale training resiliency by optimizing applications and frameworks for improved recovery from failures and preemptions - Influence architecture, design, development, and operations of next generation ML accelerator systems based on workload insights
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Answer 10 quick questions to check your fit for AIML - Staff Machine Learning Engineer - ML Efficiency, ML Platform & Technology @ Apple.
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AIML - Staff Machine Learning Engineer - ML Efficiency, ML Platform & Technology
Apple
AIML - Staff Machine Learning Engineer - ML Efficiency, ML Platform & Technology
Job Description
Do you want to shape the platform that enables the next generation of intelligent experiences on Apple products & services? In Apple’s Machine Learning Platform Technology & Infra team we have built the platform that Apple uses for developing machine learning, artificial intelligence, and computer vision applications. As a team, we have a variety of technical backgrounds, from machine learning PhDs to builders of large-scale production systems. Specifically in this role you will be working on optimizing end-to-end system performance of distributed machine learning workloads. This is a highly collaborative role and you will be working with key partners across the company. We are seeking highly motivated and experienced engineers to join our team. The ideal candidate will have a deep understanding of machine learning systems and cloud computing infrastructure. Key responsibilities in this role are: - Engage with ML researchers to optimize end-to-end performance of large scale distributed ML workloads - Analyze workload metrics to identify sources of inefficiencies and work with users to understand and optimize ML workloads - Conduct workload analysis based on benchmarking key workloads on deployed systems - Improve large scale training resiliency by optimizing applications and frameworks for improved recovery from failures and preemptions - Influence architecture, design, development, and operations of next generation ML accelerator systems based on workload insights
Locations
- Santa Clara, California, United States 95050
- Seattle, Washington, United States 98117
Salary
Estimated Salary Rangemedium confidence
8,000,000 - 15,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- machine learningintermediate
- artificial intelligenceintermediate
- computer visionintermediate
- distributed machine learningintermediate
- system performance optimizationintermediate
- cloud computing infrastructureintermediate
- workload analysisintermediate
- benchmarkingintermediate
- large scale training resiliencyintermediate
- ML accelerator systems architectureintermediate
- ML accelerator systems designintermediate
- ML accelerator systems developmentintermediate
- ML accelerator systems operationsintermediate
- collaborationintermediate
Required Qualifications
- Experience working with large scale parallel and distributed accelerator-based systems (experience)
- Experience optimizing performance and AI workloads at scale (experience)
- Experience developing code in one or more of training frameworks (such as PyTorch, TensorFlow or JAX) (experience)
- Strong communicator with ability to analyze complex and ambiguous problems (experience)
- Programming and software design skills (proficiency in C/C++ and/or Python) (experience)
- Experience working in a high-level collaborative environment and promoting a teamwork mentality (experience)
- Bachelor's degree in Computer Science and 7+ years of work experience (experience, 7 years)
Preferred Qualifications
- Deep understanding of computer systems and the interactions between HW and SW (experience)
- Experience in performance analysis and optimization experience in Cloud accelerators (experience)
- Advanced degree in CS (degree in cs)
Responsibilities
- We are seeking highly motivated and experienced engineers to join our team. The ideal candidate will have a deep understanding of machine learning systems and cloud computing infrastructure. Key responsibilities in this role are: - Engage with ML researchers to optimize end-to-end performance of large scale distributed ML workloads - Analyze workload metrics to identify sources of inefficiencies and work with users to understand and optimize ML workloads - Conduct workload analysis based on benchmarking key workloads on deployed systems - Improve large scale training resiliency by optimizing applications and frameworks for improved recovery from failures and preemptions - Influence architecture, design, development, and operations of next generation ML accelerator systems based on workload insights
Tags & Categories
Answer 10 quick questions to check your fit for AIML - Staff Machine Learning Engineer - ML Efficiency, ML Platform & Technology @ Apple.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs