AIML - Machine Learning Engineer, Foundation Models

Apple is revolutionizing artificial intelligence by developing sophisticated foundation models that power intelligent features across our product ecosystem. We're seeking skilled Machine Learning Engineers to transform cutting-edge research into scalable, production-ready AI solutions.

We are looking for engineers who are passionate about building systems that push the frontier of deep learning in terms of scaling, efficiency, and flexibility and delight millions of users in Apple products., We build frontier foundation models that power intelligent experiences at Apple. Our team works across the full training lifecycle: including pre-training foundation models, and developing mid-training approaches that bridge general capability and task-specific performance. What makes our work distinct is that we're engineering models specifically for Apple silicon and optimized for experiences that are private, personal, and deeply integrated into the OS. We're solving frontier problems in reward modeling to resist reward hacking, handling sparse and delayed rewards in agentic settings, and aligning models reliably across the spectrum from open-ended creative tasks to precise, action-taking workflows. If you're drawn to hard problems where the research and the product are inseparable, this is the team.","responsibilities":"Design and implement scalable, reliable and high-performance machine learning infrastructure for foundation models across text, image, speech, and multi-modal domains

Collaborate with other teams to productionize state-of-the-art AI algorithms

Optimize models for performance, efficiency, and on-device intelligence

Implement machine learning systems with stringent privacy and security requirements

May also be required to manage a small team of engineers.

Do you have experience in Software deployment?, Experience with foundation models and large language models

Background in multi-modal AI systems

Demonstrated ability to transform research prototypes into production systems

Published research or significant contributions to open-source ML projects

Understanding of on-device machine learning techniques

Minimum Qualifications

MS or PhD in Computer Science, Machine Learning, or related technical field

Expert-level programming skills in Python

Proficiency in machine learning frameworks such as Jax, PyTorch, TensorFlow

Strong background in: Distributed training, Model optimization, and Machine learning infrastructure

Experience with large-scale model training and deployment

Familiarity with: Kubernetes, Docker, Cloud platforms (AWS, GCP, Azure)

Distributed computing frameworks","internalDetails":null

4.14.1 out of 5 stars Cupertino, CA $181,100 - $318,400 a year, Pulled from the full job description

• Employee stock purchase plan
• Health insurance
• Retirement plan
• Dental insurance
• RSU, 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 $181,100 and $318,400, 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.