ML Researcher, Apple Foundation Models

What you'll do

• Train foundation models for reasoning, coding, and agentic capabilities using reinforcement learning.
• Develop autonomous coding agents capable of operating in real software repositories.
• Build agentic systems that handle multi-step workflows with error recovery.
• Research RL with verifiable rewards for mathematical reasoning and multi-turn coding tasks.
• Analyze scaling laws for RL compute allocation and progressive alignment across capability stages.
• Train models to manage their own context for long-horizon tasks.
• Optimize models specifically for Apple silicon and integrated OS experiences.
• Implement distillation techniques to combine independently optimized capabilities into single models.

What we're looking for

• PhD or equivalent practical experience in Computer Science or a related technical field.
• Demonstrated expertise in deep learning with publications at top ML or NLP conferences or a track record of applying deep learning to products.
• Proficient programming skills in Python and one of the deep learning toolkits such as JAX, PyTorch, or Tensorflow.
• Ability to work in a collaborative environment.
• Reinforcement learning for LLMs including RLHF, GRPO, PPO, RLVR, reward modeling, and RL scaling laws (preferred).
• Experience in code generation, coding agents, and agentic systems (preferred).
• Experience in distillation and alignment techniques (preferred).
• Experience with long context and efficiency such as sparse attention and context compression (preferred).