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Apple AIML - Staff Machine Learning Engineer ML Platform & Technology United States, Washington, Seattle 149342952
19.12.2024
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.
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
- With 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
Pay & Benefits
- At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $166,600 and $296,300, and your base pay will depend on your skills, qualifications, experience, and location.Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Apple is an equal opportunity employer that is committed to inclusion and diversity. We take affirmative action to ensure 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.
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