Machine Learning Engineer

As a Machine Learning Engineer at Apple, you will focus on developing Large Language Models (LLMs) and text processing. Your responsibilities will include: - Building, fine-tuning, optimizing, and deploying LLMs for real-world applications. - Generating scripts using LLMs and customizing models using platforms like Ollama. - Integrating LLMs into robust applications for tasks such as text generation, understanding, summarization, classification, and extraction. - Designing and implementing solutions leveraging LLMs for various business or technical workflows. - Customizing and fine-tuning LLMs using domain-specific data with tools like Ollama and LoRA/QLoRA. - Optimizing LLMs for inference speed, accuracy, and resource efficiency (quantization, pruning, etc.). - Integrating LLM functionalities into end-user applications with a focus on usability and performance. - Collaborating with product, design, and engineering teams to translate business requirements into ML-based solutions. - Developing APIs and micro-services for model serving and deployment. - Monitoring model performance and implementing continuous improvement and retraining strategies. - Staying updated with the latest research and trends in the LLM space and proposing innovative ideas. Qualifications required for this role include: - 5+ years of experience in ML/NLP with a focus on large language models and text data. - Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field. - Experience with prompt engineering, retrieval-augmented generation (RAG), and vector databases (e.g., Pinecone, FAISS). - Strong programming skills in Python, including popular ML/NLP libraries (e.g., HuggingFace Transformers, PyTorch, LangChain). - Experience with LLM customization and fine-tuning (e.g., on GPT, LLaMA, Mistral, Mixtral) and hands-on experience with Ollama or similar tools for local/custom model deployment. - Demonstrable ability to optimize model performance for production environments. - Solid grasp of REST APIs, application development, and software engineering standard processes. Preferred qualifications for the role include experience with containerization (e.g., Docker) and orchestration tools (e.g., Kubernetes), exposure to frontend/backend development for building LLM-powered dashboards or assistants, and understanding of MLOps, model versioning, and continuous improvement practices.,