AIML - Machine Learning Engineer, On-Device Machine Learning

Summary

Posted: Oct 24, 2024

Weekly Hours: 40

Role Number:200574836

Help us bring state-of-the-art ML models to the entire Apple ecosystem, enabling the next generation of ML-based experiences in a privacy-preserving way! Our team is responsible for the core framework that launches neural-network workloads on Apple devices. We build the bridge between the compute resources available on Apple hardware and an entire universe of ML models, trained by feature teams throughout Apple and by our developer community. Your work on our team will enable increasingly sophisticated models throughout our products, from the computer vision models that process every camera frame in the Apple Vision Pro, to the language models that allow human-computer interaction to feel more human. By developing the underlying representation, pipeline, and runtime executor for these workloads, including the mechanisms for mapping them to the CPU, GPU, and Neural Engine, you will play a critical role in expanding what is possible for Apple and for the world.

Description

* Design and implement improvements to Apple's Model Intermediate Language (MIL), the intermediate representation of neural-network workloads shared across the inference stack * Develop the mechanisms for analyzing and transforming MIL workloads * Build the tightly integrated pipeline that optimizes and compiles models and then orchestrates their execution on device * Optimize our software stack for extensibility and experimentation, paving the way for ongoing innovation * Collaborate with CPU, GPU, and Neural Engine hardware backends to push inference performance and efficiency * Work closely with feature teams to facilitate and debug the integration of increasingly sophisticated, state-of-the-art models

• Excellent C/C++ programming and debugging skills
• Enthusiasm for API design and software architecture
• Experience with CMake, Xcode or other equivalent build system
• Expertise with performance optimization
• Exceptional verbal and written communication skills
• BS/MS Computer Science or Electrical Engineering with Solid industry experience (5+ years)

Preferred Qualifications

• PhD in Computer Science or Electrical Engineering
• Familiarity with Machine Learning Compiler Technologies such as LLVM/MLIR
• Experience with modern neural-network architectures and deep learning libraries

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.

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.