Sr. Machine Learning Data Scientist, Productivity Apps

Summary

Posted: Dec 19, 2024

Role Number:200583878

At Apple, new ideas have a way of becoming phenomenal products, services, and customer experiences very quickly! The Productivity Apps team needs your help shaping the next generation of productivity tools by working on pioneering technologies to surprise and delight pros and enthusiasts alike. You’ll be working with our world-class teams behind iWork, Freeform, Notes, and Reminders. You’ll have the opportunity to work with world-class designers and engineers to help innovate in the productivity space in ways that only Apple can. This is a highly visible, highly impactful opportunity!

Description

As a Machine Learning Data Scientist, you’ll be responsible for driving improvements to our machine learning models through our data pipelines and model evaluations. Your contributions will be critical to our success in delivering high-quality features to our customers. You will work with a multidisciplinary team to build machine-based grading models which help reduce annotation costs and turnaround time, and to build pipelines which help automate the assessment of quality of data used for training foundational models and adapter models to power Apple products. Your work may span a variety of topics, including but not limited to: – Build and integrate grading models into our data and evaluation pipelines. – Conduct research as appropriate to improve the efficacy of our data curation methodologies. – Uncovering patterns and anomalies in our data using modern statistical and ML-based methods to model data distributions. – Employing data selection techniques such as novelty detection, active learning, and core-set selection for diverse data types like images, video, layouts, styles, etc. – Design, build, and run human evaluations of our models.

Minimum Qualifications

• Strong communication and collaboration skills with a focus on user experience.
• B.S. in Machine Learning, Computer Science, Statistics, or related areas. Or, equivalent work experience.
• 5+ years of experience with developing and evaluating ML applications, and demonstrated experience in understanding and improving data quality.
• Strong programming skills and hands-on experience using the following languages or deep learning frameworks: Python, Jax, or PyTorch.
• Experience exploring and evaluating large, semi-structured datasets.

Preferred Qualifications

• Proven expertise in machine learning with a passion for model evaluation and applications.
• Experience with natural language processing (NLP), and large language models, such as BERT, GPT, or Transformers.
• Staying on top of emerging trends in LLMs.
• Demonstrated publication record in relevant conferences (e.g. ACL, EMNLP, NeurIPS, ICML, ICLR, etc.).
• Ph.D. in Machine Learning, Computer Science, Statistics, or related areas.

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 $175,800 and $312,200, 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.