Machine Learning Engineer, Sales Engineering

Imagine what you could do here. At Apple, great ideas have a way of becoming great products, services and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other's ideas stronger. Apple's Sales Engineering team is shaping the future of Channel Sales with innovative, high-impact applications. We're looking for a Machine Learning Engineer to help us design and build the next generation of intelligent systems that power Apple's global partner ecosystem. In this role, you'll develop and deploy machine learning solutions while leveraging generative AI and advanced ML capabilities to deliver scalable, production-ready systems that accelerate strategic, high-impact initiatives across Apple Channel Sales. If you're passionate about applying AI to solve complex business problems, experimenting with emerging GenAI technologies, and building products that make a real difference, join our collaborative team and help us move fast on game-changing ideas.

Apple's Sales Engineering Rapid Application Development (RAD) team is looking for a Machine Learning Engineer to build intelligent, scalable solutions that power Apple's global Channel Sales. You'll leverage generative AI and advanced machine learning technologies to deliver high-performance, production-ready systems that drive measurable business impact. The ideal candidate blends deep ML expertise with strong engineering skills, is passionate about applying AI to solve real-world problems, and thrives in fast-paced environments delivering value quickly. You'll work side by side with product, design, and engineering teams to design, train, deploy, and optimize ML-powered applications that push the boundaries of innovation-whether enabling GenAI-driven workflows, implementing RAG-based systems, or pioneering new intelligent capabilities. If you're excited about shaping impactful AI solutions in a collaborative, experiment-driven environment, Sales Engineering RAD team is where you'll thrive.

M.S. in Computer Science, Machine Learning, Artificial Intelligence, or a closely related technical field, or equivalent practical experience.\n5+ years experience developing and deploying machine learning solutions, with a strong focus on Large Language Models (LLMs) or Large Multimodal Models (LMMs).\n5+ years experience with LLMs and transformer-based architectures (e.g., BERT, GPT, LLaMA).

Proven ability to fine-tune, adapt, and deploy LLMs/LMMs into real-world, production-grade applications.\nProficiency in Python and leading ML frameworks such as PyTorch and TensorFlow.\nHands-on experience leveraging Hugging Face Transformers and associated libraries.\nSolid understanding of Retrieval-Augmented Generation (RAG) and practical experience with orchestration frameworks like LangChain or LlamaIndex.\nFamiliarity with distributed computing, cloud platforms (AWS, Google Cloud Platform, Azure), and containerization/orchestration tools (Docker, Kubernetes).\nExceptional problem-solving skills and the ability to articulate complex ML/AI concepts clearly and effectively to diverse audiences.\nExperience extending beyond traditional LLMs/LMMs to include agent-based systems and agentic workflows.\nProficiency with advanced LLM serving and inference frameworks, ensuring scalable and efficient model deployment.\nPractical experience building sophisticated RAG applications and orchestrating complex LLM pipelines from inception to deployment.\nWorking knowledge of distributed systems and cloud-native infrastructure.\nExpertise in optimizing transformer-based architectures (e.g., BERT, GPT, LLaMA) for low-latency, high-performance inference.\nDemonstrated ability to communicate complex technical results and ML/LLM concepts with clarity and impact to both technical and non-technical stakeholders.\nExperience applying ML methodologies in specific domains, such as sales.