About the role

What will you do at Apple?

Imagine what you could do here. At Apple, great new ideas have a way of becoming

extraordinary products, services, and customer experiences very quickly. Bring

passion and dedication to your job and there's no telling what you could

accomplish! Are you passionate about music, movies, and the world of Artificial

Intelligence and Machine Learning? So are we! Join our Human-Centered AI team

for Apple Products. In this role, you'll represent the user perspective on new

features, review and analyze data, and evaluate AI models powering everything

from search and recommendations to other innovative features. Collaborate with

Data Scientists, Researchers, and Engineers to drive improvements across our

platforms.

DESCRIPTION

We are looking for an Evaluation & Insights Engineer for the Human-Centered AI

team to help evaluate and improve AI systems by combining data science, model

behavior analysis, and qualitative insights.

In this role, you will

analyze AI
outputs, develop evaluation frameworks, design qualitative, and translate
findings into actionable improvements for product and engineering teams. This
role blends deep technical expertise with strong analytical judgment to assess,
interpret, and improve the behavior of advanced AI models. You will work
cross-functionally with the Engineering and Project Managers, Product, and
Research teams to ensure that AI experience is reliable, safe, and aligned with
human expectations.
MINIMUM QUALIFICATIONS
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial
Intelligence, Cognitive Science, or a related technical field 8+ 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 .
PREFERRED QUALIFICATIONS
Knowledge of human factors, HCI, or cognitive science methodologies as applied
to AI system design.

Which skills does this role require?

PythonPyTorchJAXHugging FaceLLM EvaluationRAG ArchitecturesPrompt EngineeringRLHFDPOMLflowWeights & BiasesNLPModel AlignmentHallucination DetectionVector DatabasesSemantic SearchMachine LearningArtificial IntelligenceLLMMultimodal ModelsG-EvalDeepEvalSelfCheckGPTRAGHuman-Centered AIInference PipelinesData ScienceFine-TuningLLMs

Make your next move

Build a shortlist and prepare

Identify the requirements you can demonstrate, then choose examples from your work to discuss with the hiring team.

Build a focused shortlist before you applyCompare role requirements with your experience and give each application a clear reason.
Machine learning jobsCompare current openings and review what to look for in this role.
Practice explaining your experience in an interviewRehearse your answers before meeting the hiring team.

Review the responsibilities and requirements before adding an opening to your shortlist.