Robotics AI Engineer

AppleCupertino, CA

About The Position

Our team leverages state-of-the-art AI to better understand and communicate with users via semantic and contextual intent understanding. We are looking for a Robotics AI Engineer to bring frontier reasoning and multimodal models to life in real-time, interactive experiences. In this role, you will translate natural user intents into robust, adaptive behaviors. You will work across the full stack of applied AI/ML: prompting and evaluating frontier models, distilling them into efficient on-device models, and deploying them on Apple silicon. A successful candidate is energized by rapidly experimenting with the latest AI models, tools, and agent harnesses, and has a strong foundation in modern machine learning with hands-on experience training and fine-tuning their own models. Above all, this is a deeply product-driven team that values engineers who have shipped real products and are motivated by putting great experiences into users' hands.

Requirements

  • BS or MS in Computer Science or Machine Learning
  • Strong background in and deep understanding of modern machine learning methods
  • Experience training or fine-tuning your own transformer and/or diffusion models
  • Proficiency using coding agents such as Claude Code and Codex
  • Genuine excitement for experimenting with the latest AI models, tools, and multimodal capabilities

Nice To Haves

  • PhD in Computer Science, Machine Learning, or a related field
  • Experience with model fine-tuning and distillation for efficient, on-device deployment
  • Hands-on experience with inference engines such as llama.cpp, vLLM, MLX, or Core ML
  • Experience building agent harnesses for multi-step, closed-loop reasoning
  • Track record of shipping products to users
  • Proficiency in Python and ML frameworks (PyTorch or TensorFlow)

Responsibilities

  • Translate natural user intents into robust, adaptive behaviors.
  • Work across the full stack of applied AI/ML: prompting and evaluating frontier models, distilling them into efficient on-device models, and deploying them on Apple silicon.
  • Rapidly experiment with the latest AI models, tools, and agent harnesses.
  • Train and fine-tune their own models.
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