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Machine Learning

Apple
Posted a month ago, valid for 20 days
Location

Cupertino, CA 95015, US

Salary

$226,138 - $272,100 per year

Contract type

Full Time

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Sonic Summary

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  • Apple is seeking a candidate for a role in Cupertino, California, focused on developing innovative backend services and tools to enhance developer productivity.
  • The position requires a Bachelor’s Degree in Computer Science or a related field, along with 4 years of relevant experience.
  • Candidates should have expertise in utilizing PyTorch and JAX for machine learning algorithms, as well as experience in optimizing large language models.
  • The base pay for this role ranges from $226,138 to $272,100 per year, depending on skills, qualifications, experience, and location.
  • Apple offers comprehensive benefits, including medical coverage, retirement plans, and opportunities for employee stock ownership.
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. Working on evolving a set of innovative distributed backend services and tools that combine to enable superior developer productivity. Combining unique problem domain with established distributed system and cutting-edge ML techniques in order to achieve high performance, reliability and long term maintainability. Analyzing problem requirements, existing solutions, and available systems and making high-level architectural decisions about how to design the backend, considering both medium- and long-term goals. Developing appropriate testing and operational mechanisms. Designing new ways to scale the productivity of Apple’s own developers using innovative ML approaches and techniques. Mentoring and guiding the next generation of Apple engineers. Collaborating with team members to help refine problem requirements and work to develop ML solutions, distributed services engineering, tooling, and contributing to solutions being developed across the organization. Working with cross-functional engineering teams, identifying dependencies and opportunities with other teams. 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 $226,138 - $272,100/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.

Minimum Qualifications


Bachelor’s Degree or foreign equivalent in Computer Science or related field and 4 years of experience in the job offered or related occupation. 4 years of experience in each of the following skills is required Utilizing PyTorch and JAX to develop novel machine learning algorithms. Applying knowledge of machine learning algorithms to improve performance on classification, regression, and retrieval tasks. Utilizing data synthesis and analysis to measure the performance of machine learning models, applied towards improvement of these models. Creating tools and frameworks that support fine-tuning and reinforcement learning of Large Language Models. Optimizing tasks performed by larger models, to measure quality and operate on smaller models. Orchestrating multiple machine learning models in systems that can perform tasks not performable (or less effectively performed) by single inferences Optimizing systems that leverage large language models using methods such as caching and dynamic data rendering.

Preferred Qualifications


N/A



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