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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer- GenAI - **Company:** Apple Inc. - **Location:** Cupertino, CA, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Big Data, Cloud Computing, Data Mining, Distributed Systems, Machine Learning, Software Engineering, Workflow Management Systems, Large Language Models, Generative AI, Kubernetes, Information Technology, GPT, Docker - **Published:** August 8, 2026 - **Apply:** https://us.experteer.com/career/view-jobs/machine-learning-engineer-genai-cupertino-ca-usa-58848248 ## About the Role mentor teams (where applicable) Tasks * 3+ years in GenAI applications, ML algorithms, software engineering, and data mining with emphasis on LLMs or LMMs * Master in Artificial Intelligence, Computer Science, Statistics, Operations Research, Physics, Mechanical Engineering, Electrical Engineering or related field * experience in GenAI application building with agents and agentic workflows * proficiency with GenAI tools (e.g., Claude Code, Roo Code) * familiarity with LangChain and LlamaIndex for RAG applications and LLM orchestration * knowledge of distributed computing, cloud infrastructure, and orchestration tools (Kubernetes, Apache Airflow, Docker, Conductor, Ray) * understanding of transformer architectures (BERT, GPT, LLaMA) and low-latency inference optimization * ability to present complex ML concepts to non-technical audiences * experience applying ML to manufacturing, testing, or hardware optimization (major plus) * leadership or mentoring experience (a plus) Key requirements 4 _ ## Description Experteer Overview In this role, you will shape and execute Apple's machine learning strategy for supply chain and manufacturing systems, collaborating across functions to build scalable ML solutions. You'll drive end-to-end projects from problem framing to deployment, perform analyses for stakeholders, and partner with data engineers to deliver BI insights. You will present findings to executives and help lead teams in GenAI initiatives, emphasizing impactful, low-latency ML for manufacturing excellence. Compensation / Benefits * design and implement ML strategy for supply chain and manufacturing systems * build and advance smarter factories through scalable ML solutions * collaborate with cross-functional teams to apply algorithms to large-scale data * deliver end-to-end ML projects from problem framing to deployment * conduct ad-hoc statistical analyses * work with data engineers to produce detailed BI solutions * present analyses and insights to executives * potentially lead and mentor teams (where applicable) Tasks * 3+ years in GenAI applications, ML algorithms, software engineering, and data mining with emphasis on LLMs or LMMs * Master in Artificial Intelligence, Computer Science, Statistics, Operations Research, Physics, Mechanical Engineering, Electrical Engineering or related field * experience in GenAI application building with agents and agentic workflows * proficiency with GenAI tools (e.g., Claude Code, Roo Code) * familiarity with LangChain and LlamaIndex for RAG applications and LLM orchestration * knowledge of distributed computing, cloud infrastructure, and orchestration tools (Kubernetes, Apache Airflow, Docker, Conductor, Ray) * understanding of transformer architectures (BERT, GPT, LLaMA) and low-latency inference optimization * ability to present complex ML concepts to non-technical audiences * experience applying ML to manufacturing, testing, or hardware optimization (major plus) * leadership or mentoring experience (a plus) Key requirements * ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [The State of GenAI & Machine Learning in 2025](https://www.wearedevelopers.com/videos/1383-the-state-of-genai-machine-learning-in-2025) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Beyond GPT: Building Unified GenAI Platforms for the Enterprise of Tomorrow](https://www.wearedevelopers.com/videos/1525-beyond-gpt-building-unified-genai-platforms-for-the-enterprise-of-tomorrow) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Got AI ideas but no money? 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