Machine Learning Engineer - Generative AI

Apple Cupertino, CA

Company

Apple

Location

Cupertino, CA

Type

Full Time

Job Description

Summary

Posted: Dec 11, 2024

Weekly Hours: 40

Role Number:200582801

Are you passionate about cutting-edge generative AI? Do you thrive on transforming research into tangible products that reach millions of users? Join our team and help bring advanced AI models from the lab to people's hands!

Description

In this role, you will take a hands-on approach to the entire machine learning lifecycle-from creating and curating data to training, evaluating, and fine-tuning models using Apple's innovative ML tooling. You'll collaborate cross-functionally with teams across Apple, ensuring seamless integration of models into real-world products. Additionally, you will deploy models in production environments and build software solutions that enable workflows across the full software stack, driving innovative, user-focused experiences powered by advanced AI!

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Minimum Qualifications

  • Strong experience in machine learning model training, evaluation, and deployment, with a focus on data creation and management.
  • Proficiency in Python and familiarity with ML tooling such as PyTorch and TensorFlow.
  • Hands-on experience fine-tuning and deploying open-source models using industry-standard platforms like Hugging Face.
  • Proven ability to debug and optimize model performance through experimentation.
  • In-depth understanding of Transformer architecture and familiarity with current developments in fine-tuning large language models.
  • Demonstrated ability to work cross-functionally, collaborating with teams across the software stack to build scalable workflows.

Preferred Qualifications

  • Hands-on experience evaluating large language models (LLMs) and applying advanced metrics for performance analysis.
  • Experience generating and using synthetic data to enhance model training and evaluation.
  • Strong statistical background, including experience applying statistical methods for model evaluation and data analysis.
  • Familiarity with techniques for interpreting and explaining the behavior of deep learning models, such as attention mechanisms and feature attribution.
  • Experience with end-to-end model deployment in production environments, ensuring high-quality performance and seamless integration.

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 $143,100 and $264,200, and your base pay will depend on your skills, qualifications, experience, and location.

    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.

    Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

More

  • 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. Learn more about your EEO rights as an applicant.

Apply Now

Date Posted

12/19/2024

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