AIML - Applied Machine Learning Engineer, MIND

Seattle, Washington, United States

Machine Learning and AI

As a Machine Learning Engineer in the Machine Intelligence Neural Design (MIND) team at Apple, you will be part of the Data and Machine Learning Innovation (DMLI) organization. With roots in ML, computer vision, and energy efficiency research, MIND is strategically positioned to contribute to both short-term and long-term projects, from well-known Apple products to ambitious, high-risk, high-reward initiatives. This role focuses on shipping ML-based features and products. You will innovate in the entire end-to-end ML production pipeline, crafting creative approaches to datasets, model training, and on-device inference optimizations. We value fearless team members who are willing to try new things, iterate quickly, and prototype ideas from start to finish, resulting in high-quality implementations. You will collaborate closely with hardware, computer architecture, and software teams to drive data-driven decision-making and innovation.

Description

Your daily responsibilities will include:

  1. Analyzing and leveraging large datasets, identifying and addressing data gaps, predicting potential future gaps, and designing robust data collection strategies.
  2. Building, fine-tuning, and optimizing machine learning models, ensuring their performance is fine-tuned for on-device deployment.
  3. Developing features and models enhancing machine learning systems, including scaling up model training, building data pipelines, and optimizing performance.
  4. Reviewing and implementing cutting-edge machine learning algorithms.
  5. Working closely with cross-functional partners across the stack, including Apps, Compilation, Hardware, etc.

Minimum Qualifications

  • Proficient in Python and deep learning frameworks (e.g., PyTorch).
  • Experience training ML models, including deep learning models.
  • Expertise in large-scale data processing, distributed computing, and data engineering.
  • Able to define metrics, evaluate ML models, and conduct error analysis.
  • Bachelor's, Master's, or PhD in Computer Science or a related field.

Key Qualifications

  • Experience shipping ML features and products.
  • Familiar with cloud platforms (AWS, GCP).
  • Experience modeling vision problems in object detection, facial recognition, and/or temporal machine learning.
  • Experience building efficient ML models for on-device deployment.
  • Strong communicator with the ability to analyze complex and ambiguous problems.
  • Familiar with ML model efficiency techniques (e.g., quantization, pruning) and their inference-time bottlenecks.
  • Knowledge of recent advances in deep learning and computer vision.
  • Proven track record of contributing to diverse teams in a collaborative environment.

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 $135,400 and $250,600, 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.

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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