Frontier Robotics AI Engineer — Real-Time On-Device

Apple Inc.; Cupertino (CA); On-site; USD 185,000 - 325,000; Full time

Benefits offered by this job

Medical and dental coverage

Employee stock programs

Relocation assistance

Education reimbursement

Job summary

Apple Inc. is seeking a Robotics AI Engineer in Cupertino to translate natural user intents into robust, adaptive behaviours across the full AI/ML stack.

You will prototype frontier models, distill them into efficient on-device models, and deploy them on Apple silicon for real‑time interactive experiences. A successful candidate thrives on rapid experimentation with state-of-the-art AI models, tools, and agent harnesses, bringing a strong ML foundation and hands-on experience in training and

Qualifications

• BS or MS in Computer Science or Machine Learning.
• Strong background in modern ML methods.
• Experience training or fine-tuning transformer and/or diffusion models.
• Proficiency with coding agents such as Claude Code and Codex.
• Excitement for experimenting with AI models, tools, and multimodal capabilities.

Responsibilities

• Map user intent into closed-loop plans that adapt as context changes.
• Build agent harnesses grounding model reasoning in user context and system capabilities.
• Leverage multimodal models to understand user intent across audio, text, and vision.
• Translate product requirements into metrics and use findings to improve models.
• Fine-tune and distill models for on-device deployment.
• Deploy and optimize models on Apple silicon for real-time performance.
• Prototype and assess new AI models and agent harnesses to advance product capabilities.

Skills

ML fundamentals

Deep learning

Transformer models

Agent systems

Multimodal ML

Education

BS or MS in Computer Science or ML

Tools

llama.cpp

vLLM

Core ML

PyTorch

TensorFlow

Job description

Apple Inc. is seeking a Robotics AI Engineer in Cupertino to translate natural user intents into robust, adaptive behaviours across the full AI/ML stack.

You will prototype frontier models, distill them into efficient on-device models, and deploy them on Apple silicon for real‑time interactive experiences. A successful candidate thrives on rapid experimentation with state-of-the-art AI models, tools, and agent harnesses, bringing a strong ML foundation and hands-on experience in training and