Machine Learning Engineer

Imagine what you can do here. Apple is a place where extraordinary people gather to do their lives best work. Together we create products and experiences people once couldn't have imagined, and now, can't imagine living without. It's the diversity of those people and their ideas that inspires the innovation that runs through everything we do.

Description

APPLE INC has the following available in Cupertino, California and various unanticipated locations throughout the USA. Design and develop innovative software systems for ads recommender systems that run on high performance CPU/GPUs (graphics processing units). Create modern machine learning solutions using natural language processing methods and techniques such as Transformers, Markov Chains and Self Attention. Read research papers, follow recent machine learning research in related area, prototype presented ideas and propose possible applications to team's business. Write code in Java, SQL, and Python packages such as PyTorch and Tensorflow to apply the machine learning models and algorithms in the research papers to team's business. Help maintain the codes using version control systems such as git used by the Ad Platform and fix emergent bugs and errors. Dig useful information and design features from a huge amount of data and errors. Collaborate with technical backend engineers to ensure the fidelity and high performance standards of artifacts developed by ML/ Algorithms engineers through integration and performance tests before deploying to production. Domestic Travel Required: 20%. 40 hours/week. 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 $199,534 - $264,200/yr and your base pay will depend on your skills, qualifications, experience, and location. PAY & BENEFITS: Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits: https://www.apple.com/careers/us/benefits.html. Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

• Master's degree or foreign equivalent in Computer Science or related field and 6 months of experience in the job offered or related occupation.
• Experience and/or education must include:
• Using Python, Java, or Go to develop and implement machine learning algorithms, build scalable services, and manage data processing tasks.
• Using machine learning frameworks and tools including Tensorflow, PyTorch, and Hive to design, train, and deploy modern machine learning models, and manage large-scale data analyses.
• Working with distributed file systems such as HDFS to store, retrieve, and manage the data required for machine learning models, applications, and analyses.
• Utilizing software version control systems (Git) and Linux to collaborate on software development projects, manage code versions, and configure and maintain the software environment.
• Developing and deploying machine learning models for real-world applications, particularly in the field of Natural Language Processing (NLP).
• Handling large datasets and implementing text classification models to solve complex problems.
• Leveraging knowledge of data/web mining, recommender systems, and deep learning to extract insights and patterns from large datasets, optimize performance, implement cutting-edge deep learning models, and streamline the machine learning lifecycle from experimentation to production.

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