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AIML - Machine Learning Engineer , Apple Foundation Models
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What they do
A Machine Learning Engineer specializes in designing, building, and deploying machine learning models. They utilize statistical and mathematical techniques, parallelizing processing, hyperparameter tuning, and other optimization methodologies to improve model performance. Responsibilities also include collecting and preprocessing large datasets, conducting exploratory data analysis, working closely with data engineers to understand data requirements, and engineer input variables for machine learning models.
$126,465 / year median in North Carolina
Job Description
Apple Foundation Models Cary, North Carolina, United States Machine Learning and AI Summary Posted:
Sep 08, 2026Weekly Hours:
40Role Number:
200668999-1435 We are a tight-knit group of researchers and engineers responsible for building large scale frontier foundation models at Apple. We believe the most interesting breakthroughs in deep learning happen when we bridge the gap between raw model capability and user-centric utility.In this role, you will play a critical role shaping the future of our LLM efforts, specifically in transforming our models into highly capable, intelligent assistants that power billions of Apple products. You will tackle core training challenges in instruction following, tool use, deep reasoning, and architectural adaption — designing models that deliver magical, deeply integrated, and privacy-forward experiences across the Apple ecosystem. You will work alongside a fast-growing team of world-class experts to explore novel training strategies, architectural adaptations, and advanced evaluation methodologies. Description
- Design and iterate on end-to-end post-training strategies (including Reinforcement Learning) to unlock model capacities toward achieving specific model behaviors.
- Pioneer novel algorithms for preference optimization, model steering, and safety.
- Drive our data strategy by researching methods for high-quality human and synthetic data generation, automated data filtering, and curriculum learning to improve instruction following and reasoning.
- Design robust evaluation methodologies to measure model helpfulness, factuality, and utility, moving beyond static benchmarks to accurately capture real-world performance.
- Partner closely with pre-training teams to inform architecture choices, and with product teams to translate user requirements into model capabilities.
Benefits
- Dental Insurance