Machine Learning Engineer, Sales Engineering

Design and deploy production-grade LLM and RAG systems for Apple’s global Channel Sales ecosystem.

Responsibilities

• Design, build, and deploy scalable machine learning and generative AI systems for Apple’s global Channel Sales ecosystem.
• Develop and optimize ML pipelines using LLMs, LMMs, and RAG-based architectures for production applications.
• Partner with cross-functional teams to translate business needs into intelligent, data-driven workflows.
• Fine-tune and evaluate transformer-based models (e.g., GPT, LLaMA, BERT) to improve accuracy, performance, and scalability.
• Prototype and productionize emerging AI capabilities, including agentic workflows and generative assistants.
• Apply MLOps practices for model training, deployment, monitoring, and continuous improvement.
• Support secure and compliant handling of sensitive data (including PII) while aligning with Apple’s privacy standards.

Requirements

• Proven ability to fine-tune, adapt, and deploy LLMs/LMMs into real-world, production-grade applications.
• Strong proficiency in Python and major ML frameworks including PyTorch and TensorFlow.
• Hands-on experience with Hugging Face Transformers and related libraries.
• Solid understanding of Retrieval-Augmented Generation (RAG) and practical experience with LangChain or LlamaIndex.
• Familiarity with distributed computing, AWS, GCP, and Azure, plus Docker and Kubernetes.
• Exceptional problem-solving skills and the ability to communicate complex ML/AI concepts clearly to diverse audiences.
• Experience extending LLM/LMM work to agent-based systems and agentic workflows.
• Proficiency with advanced LLM serving and inference frameworks for scalable, efficient deployment.
• Practical experience building sophisticated RAG applications and orchestrating LLM pipelines from inception to deployment.
• Working knowledge of distributed systems and cloud-native infrastructure.
• Expertise optimizing transformer-based architectures (e.g., BERT, GPT, LLaMA) for low latency, high-performance inference.
• Demonstrated ability to present technical results and ML/LLM concepts with clarity and impact to technical and non-technical stakeholders.
• Experience applying ML methodologies in domains such as sales.

Minimum Qualifications

• MS / BS in Computer Science, Machine Learning, Artificial Intelligence, or a closely related technical field, or equivalent experience.
• 5+ years experience developing and deploying machine learning solutions with a focus on LLMs or LMMs.
• 5+ years experience with LLMs and transformer-based architectures (e.g., BERT, GPT, LLaMA).

Technologies

• Python
• PyTorch
• TensorFlow
• Hugging Face Transformers
• LangChain
• LlamaIndex
• LLMs
• LMMs
• RAG
• GPT
• LLaMA
• BERT
• Transformer-based architectures
• Docker
• Kubernetes
• AWS
• GCP
• Azure

Role Context

• Apple’s Sales Engineering Rapid Application Development (RAD) team is hiring a Machine Learning Engineer to build intelligent, scalable solutions for Apple’s global Channel Sales.
• Work spans generative AI and advanced ML technologies to deliver high-performance, production-ready systems with measurable business impact.
• Collaborate with product, design, and engineering teams to design, train, deploy, and optimize ML-powered applications, including GenAI-driven workflows and RAG-based systems.

Location: Austin, TX (onsite)