Machine Learning Engineer, (Applied Machine Learning), AI & Data Platforms (AiDP)
Apple
21 hours ago
Full-time
On-site
Sunnyvale, California, United States
ML & AI Engineering
Imagine what you could do here. At Apple, revolutionary ideas have a way of becoming
extraordinary products, services, and customer experiences. Join the Ai Data Platform
Applied Machine Learning team to pioneer enterprise solutions where generative AI meets
Apple's unique commitment to privacy-first innovation. Together, we'll create tools that
redefine industries while safeguarding what matters most – our users' trust.
As a pivotal member of Apple's enterprise generative AI efforts, you will help design, build, and evolve models, tools and applications that power high-impact AI experiences across the company. You will contribute to the architecture and optimization of AI/gen AI systems built for high availability, scalability, and reliability, working across backend services and application layers. You would solve AI problems in gen AI Safety, machine translation, content understanding, multi-modality, multi-agent systems, fine tuning and more. Our team designs and implements SOTA AI Models, services, and AI platform components that advance adoption of gen AI at apple. We tackle unique AI challenges in AI Safety, privacy-preserving generations, efficient inference, and multimodal integration, while enabling teams to build on top of our foundations. We deliver production-grade systems and models that meet Apple's rigorous standards for quality, performance, and scalability.
Bachelor of Science in Computer Science, Machine Learning, or a related quantitative field or equivalent experience 2+ years of hands-on experience in applied AI/machine learning work in industry or 4+ years of hands on AI research and development experience in academia Demonstrated expertise in generative AI, computer vision, natural language processing, or general machine learning with a passion for problem solving.
MS or Phd in Machine Learning, Natural Language Processing, Computer Vision or relatesd areas strongly preferred Experience in ML frameworks for training, fine-tuning, and deploying ML/generative models at scale Proven track record of building large scale, enterprise-grade ML/Gen-AI products in cloud environments (AWS, GCP , Azure) or on-prem infrastructure
Description
As a pivotal member of Apple's enterprise generative AI efforts, you will help design, build, and evolve models, tools and applications that power high-impact AI experiences across the company. You will contribute to the architecture and optimization of AI/gen AI systems built for high availability, scalability, and reliability, working across backend services and application layers. You would solve AI problems in gen AI Safety, machine translation, content understanding, multi-modality, multi-agent systems, fine tuning and more. Our team designs and implements SOTA AI Models, services, and AI platform components that advance adoption of gen AI at apple. We tackle unique AI challenges in AI Safety, privacy-preserving generations, efficient inference, and multimodal integration, while enabling teams to build on top of our foundations. We deliver production-grade systems and models that meet Apple's rigorous standards for quality, performance, and scalability.
Minimum Qualifications
Bachelor of Science in Computer Science, Machine Learning, or a related quantitative field or equivalent experience 2+ years of hands-on experience in applied AI/machine learning work in industry or 4+ years of hands on AI research and development experience in academia Demonstrated expertise in generative AI, computer vision, natural language processing, or general machine learning with a passion for problem solving.
Preferred Qualifications
MS or Phd in Machine Learning, Natural Language Processing, Computer Vision or relatesd areas strongly preferred Experience in ML frameworks for training, fine-tuning, and deploying ML/generative models at scale Proven track record of building large scale, enterprise-grade ML/Gen-AI products in cloud environments (AWS, GCP , Azure) or on-prem infrastructure