Job description

We are looking for a Machine Learning Engineer focused on Evaluation & Insights for the Human-Centered AI team. In this role, you will bridge the gap between human perception and algorithmic performance, helping evaluate and optimize Foundation Models and generative AI systems. You will architect robust evaluation frameworks, design scalable MLOps pipelines for model assessment, and translate qualitative failure modes into programmatic guardrails and training signals (e.g., SFT, RLHF/DPO). This role blends deep ML engineering expertise with strong analytical judgment to assess, interpret, and improve the behavior of advanced AI models. You will work cross-functionally with Software Engineering, Product, Research and Responsible AI teams at Apple to ensure that our AI experiences are reliable, safe, and aligned with human expectations. 5+ years of relevant industry experience in ML Engineering or Applied Research. Advanced proficiency in Python and modern deep learning ecosystems (PyTorch, JAX, Hugging Face). Proven experience building scalable ML inference pipelines, model-evaluation workflows, and structured rating frameworks for large-scale AI systems. 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). 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. Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Cognitive Science, or a related technical field Knowledge of human factors, HCI, or cognitive science methodologies as applied to AI system design.

Requirements

Must-haves

5+ years of relevant industry experience in ML Engineering or Applied Research.
Advanced proficiency in Python and modern deep learning ecosystems (PyTorch, JAX, Hugging Face).
Proven experience building scalable ML inference pipelines, model-evaluation workflows, and structured rating frameworks for large-scale AI systems.
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).
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.
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Cognitive Science, or a related technical field
Knowledge of human factors, HCI, or cognitive science methodologies as applied to AI system design.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaimlpythonllm

Platform: apple
Effort: medium