Apple is hiring a Machine Learning Engineer for Sales Engineering in Austin, Texas (onsite). In this role, you will design and deploy scalable machine learning and generative AI systems that support Apple’s global Channel Sales ecosystem, with a focus on production outcomes.
What You’ll Do
- Design, build, and deploy scalable machine learning and generative AI solutions for Apple’s global Channel Sales ecosystem.
- Develop and optimize ML pipelines that use LLMs, LMMs, and Retrieval-Augmented Generation (RAG) architectures for production-grade applications.
- Partner with cross-functional teams to convert business needs into intelligent, data-driven systems and workflows.
- Fine-tune and evaluate transformer-based models (for example, GPT, LLaMA, and BERT) to improve accuracy, performance, and scalability.
- Prototype and productionize emerging AI capabilities, including agentic workflows and generative assistants.
- Apply MLOps best practices across training, deployment, monitoring, and continuous improvement.
- Support secure and compliant handling of sensitive data, including PII, while maintaining Apple’s privacy standards.
Technologies
Python, PyTorch, TensorFlow, Hugging Face Transformers, LangChain, LlamaIndex, LLMs, LMMs, RAG, GPT, LLaMA, BERT, transformer-based architectures, Docker, Kubernetes, AWS, GCP, Azure.
Role Description
Apple’s Sales Engineering Rapid Application Development (RAD) team is seeking a Machine Learning Engineer to build intelligent, scalable solutions for Apple’s global Channel Sales. You will use generative AI and advanced machine learning techniques to deliver production-ready systems with measurable business impact. Work will include collaborating with product, design, and engineering teams to design, train, deploy, and optimize ML-powered applications, including GenAI-driven workflows, RAG-based systems, and new intelligent capabilities.
Minimum Qualifications
- MS / BS in Computer Science, Machine Learning, Artificial Intelligence, or a closely related technical field, or equivalent experience.
- 5+ years of experience developing and deploying machine learning solutions, with a strong focus on Large Language Models (LLMs) or Large Multimodal Models (LMMs).
- 5+ years of experience with LLMs and transformer-based architectures (for example, BERT, GPT, LLaMA).
Additional 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 TensorFlow.
- Hands-on experience leveraging Hugging Face Transformers and associated libraries.
- Solid understanding of Retrieval-Augmented Generation (RAG) and practical experience with orchestration frameworks such as LangChain or LlamaIndex.
- Familiarity with distributed computing, cloud platforms (AWS, GCP, Azure), and containerization/orchestration tools (Docker, Kubernetes).
- Exceptional problem-solving skills, including the ability to explain complex ML/AI concepts 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 for scalable, 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 (for example, BERT, GPT, LLaMA) for low latency, high-performance inference.
- Demonstrated ability to communicate complex technical results and ML/LLM concepts clearly to both technical and non-technical stakeholders.
- Experience applying ML methodologies in specific domains, such as sales.
Location
Austin, TX (onsite)
