Machine Learning Engineer, Sales Engineering

Overview

Summary 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.

Description 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.

Responsibilities

• Design, build, and deploy scalable machine learning and generative AI solutions that power Apple’s global Channel Sales ecosystem.

• Develop and optimize ML pipelines leveraging LLMs, LMMs, and RAG-based architectures for production-grade applications.

• Collaborate with cross-functional teams to translate business needs into intelligent, data-driven systems and workflows.

• Fine-tune and evaluate transformer-based models (e.g., GPT, LLaMA, BERT) for accuracy, performance, and scalability.

• Prototype and product ionize emerging AI capabilities, including agentic workflows and generative assistants.

• Apply MLOps best practices for model training, deployment, monitoring, and continuous improvement.

• Ensure secure, compliant handling of sensitive data (including PII) while maintaining Apple’s privacy standards.

Minimum Qualifications

• M.S. in Computer Science, Machine Learning, Artificial Intelligence, or a closely related technical field, or equivalent practical experience.

• 5+ years experience developing and deploying machine learning solutions, with a strong focus on Large Language Models (LLMs) or Large Multimodal Models (LMMs).

• 5+ years experience with LLMs and transformer-based architectures (e.g., BERT, GPT, LLaMA).

Preferred Qualifications

• Proven ability to fine-tune, adapt, and deploy LLMs/LMMs into real-world, production-grade applications.

• Proficiency in Python and leading ML frameworks such as PyTorch and Tensor Flow.

• Hands-on experience leveraging Hugging Face Transformers and associated libraries.

• Solid understanding of Retrieval-Augmented Generation (RAG) and practical experience with orchestration frameworks like Lang Chain or Llama Index.

• Familiarity with distributed computing, cloud platforms (AWS, GCP, Azure), and containerization/orchestration tools (Docker, Kubernetes).

• Exceptional problem-solving skills and the ability to articulate complex ML/AI concepts clearly and effectively to diverse audiences.

• Experience extending beyond traditional LLMs/LMMs to include agent-based systems and agentic workflows.

• Proficiency with advanced LLM serving and inference frameworks, ensuring scalable and efficient model deployment.

• Practical experience building sophisticated RAG applications and orchestrating complex LLM pipelines from inception to deployment.

• Working knowledge of distributed systems and cloud-native infrastructure.

• Expertise in optimizing transformer-based architectures (e.g., BERT, GPT, LLaMA) for low-latency, high-performance inference.

• Demonstrated ability to communicate complex technical results and ML/LLM concepts with clarity and impact to both technical and…