Machine Learning Engineer AI & ML Evaluation Frameworks

The Health Sensing Machine Learning Interpretability u0026 Analytics (MLIA) team ensures clinical rigor and contextual trust are at the foundation of Apples health sensing features. We are looking for an exceptional ML Engineer to help us build the next generation of scalable evaluation infrastructure and lead rigorous investigations into model performance. You will develop cutting-edge tools synthetic data pipelines and automated frameworks that ensure our health features are mathematically sound demographically equitable and clinically safe. If you are passionate about AI safety robust software architecture and pushing the boundaries of ML innovation come join us!

In this role you will architect and build large-scale evaluation frameworks to interrogate unimodal ML systems and multi-modal foundation models. Beyond infrastructure you will lead deep-dive ML evaluations performing failure analysis to uncover performance gaps reasoning flaws and edge cases. You will translate findings into actionable insights and work directly with algorithm teams to improve the safety and reliability of our health features. Your work will empower teams across Apple to rapidly evaluate multi-modal sensor fusion while upholding Apples privacy standards.

Design robust methodologies and scalable frameworks to assess the performance reliability and safety of both traditional ML and foundation models (e.g. LLMs diffusion models). nDrive failure analysis along with building instrumentation to detect clinical hallucinations reasoning flaws and edge cases. nExpand LLM/diffusion-based data generation pipelines that enable model training and evaluation without exposing real user data adaptors and visualizers to fuse asynchronous time-series signals (wearables camera behavioral metadata).nDevelop generalizable tools and metrics to discover biases and measure demographic equity across diverse populationsnTranslate evaluation results into actionable engineering insights for GenAI researchers algorithm leads and clinical experts.

BS in Computer Science Machine Learning Statistics or related fieldn3 years of experience in ML Engineering or Applied MLnStrong experience in evaluating supervised unsupervised LLMs and deep learning in Python with the ability to write production-grade code (OOP CI/CD Git)nHands-on experience in failure analysis evaluating LLMs and driving subsequent model improvementsnExperience building data pipelines inference frameworks and automated evaluation systemsnStrong communication skills to articulate complex technical concepts across technical and non-technical audiences

MS/PhD in Computer Science Machine Learning Statistics or related fieldnExperience evaluating LLMs or agentic systems (e.g. LLM-as-a-judge RAG evaluation)nExperience with synthetic data generation and prompt engineeringnExperience in parallel data processing (Spark Kubernetes Airflow) or privacy-preserving ML (Federated Learning)nBackground in AI Safety model interpretability or adversarial testingnInterest in digital health and clinical rigor