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Staff Machine Learning Engineer
Apple
H1B ✓On-sitestaffPosted April 22, 2026
About the Role
You will design and build scalable ML infrastructure and data platforms to support generative AI and intelligent product development. Your role involves managing the full ML lifecycle, from experimentation to production deployment, while optimizing model performance and reliability.
Requirements
Candidates must have a strong foundation in machine learning and extensive experience building large-scale distributed systems. Proficiency in Python, Java, or Go is required, along with a degree in Computer Science or a related field.
Full Job Description
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 build robust systems that connect scalable data pipelines with advanced ML workflows, accelerating the development of real-world AI applications. Our work spans the full ML lifecycle, from experimentation to deployment, and you’ll play a key role in shaping how AI models are built, optimized, and scaled. We develop a platform for ML data and features that powers advanced GenAI applications. This includes embeddings (generation, evaluation, ANN search, multimodal support), AI Ops, efficient inference, and a modern feature platform designed to streamline experimentation and drive innovation. We’re looking for engineers and researchers passionate about generative models, data-centric ML, and intelligent systems across diverse real-world use cases. With the autonomy to experiment, the scale to make an impact, and the support to take ideas from prototype to production, you’ll work alongside a world-class team to build intelligent, flexible systems that make ML development faster, more reliable, and more creative.
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.
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 Expertise in building and running large scale distributed systems 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
Experience in the below is preferred: Proficiency in one or more ML frameworks Experience with containerization and orchestration technologies, such as Docker and Kubernetes.
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.
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 Expertise in building and running large scale distributed systems 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
Experience in the below is preferred: Proficiency in one or more ML frameworks Experience with containerization and orchestration technologies, such as Docker and Kubernetes.