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Apple Applied ML Engineer– Foundation Models & Multimodal Intelligence United States, California, Sunnyvale 74255158
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As an ML Engineer, you will drive the development of large-scale training and inference pipelines for foundation models, enabling agentic system to adapt to Apple’s diverse ecosystem. You will design and optimize data workflows that handle vast multimodal datasets, ranging from text and images to other sensory data, and build evaluation frameworks that measure not just raw performance, but reasoning capabilities and task success in real-world scenarios.
- BS and a minimum of 3 years relevant industry experience.
- Proficiency in Python and at least one deep learning framework (PyTorch, JAX, or TensorFlow).
- Hands-on experience with large-scale distributed training pipelines and data engineering for ML.
- Software engineering skills, including modular design, testing, and performance optimization.
- Master’s, or PhD in Computer Science, Machine Learning, or related technical field, or equivalent industry experience.
- Strong experience in machine learning engineering, with expertise in large-scale training, fine-tuning, or deployment of deep learning models.
- Experience with foundation models (language, vision, or multimodal).
- Familiarity with agentic or reasoning-based model pipelines, such as tool-use orchestration, chain-of-thought reasoning, or planning systems.
- Expertise in large-scale data pipelines (data curation, preprocessing, and efficient storage).
- Experience evaluating and optimizing models for multimodal reasoning and task completion.
- Knowledge of retrieval-augmented generation (RAG), personalization, or grounding techniques.
- Experience optimizing models for production deployment (latency, memory, quantization, or on-device adaptation).
- Familiarity with privacy-preserving ML or federated learning.
- Ability to collaborate effectively with researchers and product engineers in a cross-functional environment.
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
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