Staff Machine Learning Engineer,Apple ML Data Platform

Summary

Posted:Apr 11, 2025

Role Number:200598686

Join a team at the forefront of ML infrastructure and generative AI, where data and model workflows come together to enable the next generation of intelligent experiences on Apple products and services. We focus on building robust systems that connect scalable data pipelines with advanced ML workflows to accelerate the development of real-world AI applications. You’ll have the opportunity to work on a variety of exciting challenges, from multimodal data models (text, images, audio, and more) to developing cutting-edge generative AI solutions. This includes accelerating data-centric ML, improving model performance through intelligent data workflows, and experimenting with novel approaches like synthetic data generation and automated labeling. Our work spans the entire ML lifecycle, from experimentation to deployment, and you’ll play a key role in shaping how AI models are built, optimized, and scaled. We are looking for engineers and researchers who are passionate about pushing the boundaries of generative models, data-centric ML, and intelligent systems across a range of real-world use cases. You’ll have the autonomy to experiment, the scale to make impact, and the support to take ideas from prototype to production. This is a unique opportunity to work at the intersection of cutting-edge infrastructure and ML research, helping teams across the company accelerate how machine learning is developed, deployed, and applied in practice. You’ll work alongside a world-class team of engineers and researchers to build robust, flexible, and intelligent systems that make ML development faster, more reliable, and more creative.

Description

The ADP ML Data Platform team enables future Apple intelligent products by providing Apple engineers with cutting edge ML technologies, large scale compute and data systems specifically designed for machine learning. As a member of the Apple ML Data Platform team, your responsibilities will include: * Prototype and optimize GenAI models, including open-source models, for scalable production use * Build a platform that enables teams to easily configure models, apply tuning strategies (e.g., LoRA/QLoRA), perform quantization, and get models production-ready for scalable deployment * Continuously improve platform capabilities to handle next-gen ML workloads, including foundation models and retrieval-augmented systems * Use ML techniques to drive smarter data workflows – including synthetic data generation, automated labeling, active learning, and data curation * Collaborate across research and engineering teams to accelerate experimentation * Collaborate closely with teams across the stack to enable high-quality, end-to-end ML experiences * Use and extend tools built on modern ML frameworks * Optimize platform components for large-scale ML workloads across distributed systems * Diagnose, fix, improve, and automate complex issues across the entire stack to ensure maximum uptime and performance

Minimum Qualifications

• Strong foundation in machine learning, with hands-on experience across the end-to-end ML workflow – including data preparation, pipeline development, experimentation, evaluation, and deployment
• Familiarity with modern generative techniques (e.g. transformers, diffusion, retrieval-augmented generation)
• Proven experience building and delivering data and machine learning infrastructure in real-world production environments
• Familiarity with fine-tuning workflows, model optimization, and preparing models for scalable inference.
• Familiarity with generative AI and its applications in accelerating and enhancing machine learning workflows
• Experience configuring, deploying and troubleshooting large scale production environments
• Experience in designing, building, and maintaining scalable, highly available systems that prioritize ease of use
• Extensive programming experience in Java, Python or Go
• Strong collaboration and communication (verbal and written) skills
• Comfortable navigating ambiguity and evolving technical landscapes, especially in fast-moving areas
• B.S., M.S., or Ph.D. in Computer Science, Computer Engineering, or equivalent practical experience

Preferred Qualifications

• Proficiency in one or more ML frameworks
• Experience with containerization and orchestration technologies, such as Docker and Kubernetes.

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 $166,600 and $296,300, 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.