Applied ML Engineer– Foundation Models & Multimodal Intelligence

Key Skills

JAX framework, Machine learning techniques, Optimization, PyTorch, TensorFlow

Job Description

As an ML Engineer, you will drive the development of large-scale training and inference pipelines for foundation models, enabling agentic system to adapt to Apple’s diverse ecosystem. You will design and optimize data workflows that handle vast multimodal datasets, ranging from text and images to other sensory data, and build evaluation frameworks that measure not just raw performance, but reasoning capabilities and task success in real-world scenarios.

Work hand-in-hand with researchers to translate cutting-edge advances, such as new alignment, grounding, and reasoning techniques—into production-grade implementations, striking a balance between rapid experimentation and solid engineering practices. Strong software engineering principles, from reproducible training setups to reliable CI/CD for ML, will be critical to ensure these models scale to millions of users while meeting Apple’s high standards for efficiency, security, and privacy. This role is ideal for engineers who are passionate about turning foundation models into capable, reasoning agents, who thrive at the interface between research and large-scale systems, and who are excited to define how multimodal intelligence will shape the next generation of Apple products.

Minimum Qualifications

• BS and a minimum of 3 years relevant industry experience.
• Proficiency in Python and at least one deep learning framework (PyTorch, JAX, or TensorFlow).
• Hands-on experience with large-scale distributed training pipelines and data engineering for ML.
• Software engineering skills, including modular design, testing, and performance optimization.

Preferred Qualifications

• Master’s, or PhD in Computer Science, Machine Learning, or related technical field, or equivalent industry experience.
• Strong experience in machine learning engineering, with expertise in large-scale training, fine-tuning, or deployment of deep learning models.
• Experience with foundation models (language, vision, or multimodal).
• Familiarity with agentic or reasoning-based model pipelines, such as tool-use orchestration, chain-of-thought reasoning, or planning systems.
• Expertise in large-scale data pipelines (data curation, preprocessing, and efficient storage).
• Experience evaluating and optimizing models for multimodal reasoning and task completion.
• Knowledge of retrieval-augmented generation (RAG), personalization, or grounding techniques.
• Experience optimizing models for production deployment (latency, memory, quantization, or on-device adaptation).
• Familiarity with privacy-preserving ML or federated learning.
• Ability to collaborate effectively with researchers and product engineers in a cross-functional 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 $147,400 and $272,100, 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.