Apple
AIML - Staff Machine Learning Engineer, ML Platform & Technology
Apple, Seattle, Washington, us, 98127
AIML - Staff Machine Learning Engineer, ML Platform & Technology
Seattle, Washington, United States
Machine Learning and AI
Want to build the training platform that engineers rely on to develop next-generation Apple Intelligence products and services? As a machine learning engineer on our team, you will create software systems and algorithms to enable performant, scalable training for Apple’s AI-driven experiences. Join our team of highly skilled, impact-focused engineers! This role also includes opportunities to open source your work and publish at top ML conferences.
Description
We're looking for strong machine learning engineers to help build next-generation tools for training deep learning models at scale. You'll be part of a team of training technology experts, focusing on training speed and scalability. We're looking for candidates with polished coding skills as well as passion for machine learning and computational science. In exchange, we offer a respectful work environment, flexible responsibilities, and access to world-class experts and growth opportunities—all at one of the best companies in the world. Design and develop components for our centralized, scalable ML platform. Push the limits of existing solutions for large-scale training. Develop novel techniques to circumvent the limitations of these solutions. Deploy your techniques on high-impact tasks from our partners across the company building new Apple Intelligence products and services. We encourage publishing novel work at top ML conferences and releasing contributions as open source. Minimum Qualifications
Strong Python programming skills Understanding of data structures, software design principles, and algorithms Experience with deep learning frameworks, such as PyTorch, or JAX Experience building large-scale distributed systems Familiarity with parallelization algorithms for large model training Familiarity with recent developments in foundation model architectures Minimum of 7+ years of industry experience Masters in the area of Computer Science or equivalent, or a related domain Preferred Qualifications
Publication record at ML conferences such as MLSys, NeurIPS, etc. Experience developing model parallel and data parallel training solutions and other training optimizations Experience with parallel training libraries such as torch.distributed, DeepSpeed, or FairScale Experience building large-scale deep learning infrastructure or platforms for distributed model training PhD in the area of Computer Science or equivalent, or a related domain
#J-18808-Ljbffr
We're looking for strong machine learning engineers to help build next-generation tools for training deep learning models at scale. You'll be part of a team of training technology experts, focusing on training speed and scalability. We're looking for candidates with polished coding skills as well as passion for machine learning and computational science. In exchange, we offer a respectful work environment, flexible responsibilities, and access to world-class experts and growth opportunities—all at one of the best companies in the world. Design and develop components for our centralized, scalable ML platform. Push the limits of existing solutions for large-scale training. Develop novel techniques to circumvent the limitations of these solutions. Deploy your techniques on high-impact tasks from our partners across the company building new Apple Intelligence products and services. We encourage publishing novel work at top ML conferences and releasing contributions as open source. Minimum Qualifications
Strong Python programming skills Understanding of data structures, software design principles, and algorithms Experience with deep learning frameworks, such as PyTorch, or JAX Experience building large-scale distributed systems Familiarity with parallelization algorithms for large model training Familiarity with recent developments in foundation model architectures Minimum of 7+ years of industry experience Masters in the area of Computer Science or equivalent, or a related domain Preferred Qualifications
Publication record at ML conferences such as MLSys, NeurIPS, etc. Experience developing model parallel and data parallel training solutions and other training optimizations Experience with parallel training libraries such as torch.distributed, DeepSpeed, or FairScale Experience building large-scale deep learning infrastructure or platforms for distributed model training PhD in the area of Computer Science or equivalent, or a related domain
#J-18808-Ljbffr