New Grad Machine Learning Jobs in Round Rock, TX

Austin Metro Area, Texas, United States Software and Services

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.

• 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 productionize 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.

• 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).

• 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 like LangChain or LlamaIndex.
• 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 non‑technical stakeholders.
• Experience applying ML methodologies in specific domains, such as sales.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant

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Apple accepts applications to this posting on an ongoing basis.