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 Media Services. 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. You'll

also collaborate with Data Scientists, Researchers, and Engineers to drive

improvements across our platforms.

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.
MINIMUM QUALIFICATIONS
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
PREFERRED QUALIFICATIONS
Knowledge of human factors, HCI, or cognitive science methodologies as applied
to AI system design.

Which skills does this role require?

PythonPyTorchJAXHugging FaceMLOpsLLMsNLPRLHFDPOPrompt EngineeringRAGVector DatabasesMLflowWeights & BiasesMachine LearningData ScienceArtificial IntelligenceFoundation ModelsGenerative AILLMHuman-Centered AIModel EvaluationInference PipelinesHallucination DetectionSemantic SearchFine-TuningResponsible AISoftware EngineeringProduct Development

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.