Machine Learning Engineer

Requirements

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Cognitive Science, or a related technical field, with relevant industry experience in ML Engineering or Applied Research.
• Advanced proficiency in Python and modern deep learning ecosystems (PyTorch, JAX, Hugging Face).
• Strong ability to interpret unstructured model outputs (text, transcripts, embedding spaces) and synthesize qualitative findings into actionable engineering guidance and training objectives.
• Hands-on experience developing, fine-tuning, or evaluating LLMs, multimodal models, and NLP systems.
• Deep familiarity with AI quality metrics, hallucination detection techniques (e.g., SelfCheckGPT), model alignment (RLHF/DPO), and LLM-as-a-judge frameworks (e.g., G-Eval, DeepEval).

Nice to Haves

• Knowledge of human factors, HCI, or cognitive science methodologies as applied to AI system design.
• Proven experience building scalable ML inference pipelines, model-evaluation workflows, and structured rating frameworks for large-scale AI systems.
• Experience building internal tools or automated pipelines for ML workflows using tools like MLflow, Weights & Biases, or similar platforms.
• Strong familiarity with advanced prompt engineering, RAG architectures (vector databases, semantic search), and Fine-Tuning.

What You'll Be Doing

• Bridge the gap between human perception and algorithmic performance.
• Evaluate and optimize Foundation Models and generative AI systems.
• Architect robust evaluation frameworks.
• Design scalable MLOps pipelines for model assessment.
• Collaborate cross-functionally with Software Engineering, Product, Research, and Responsible AI teams at Apple.