Summary
Join our multidisciplinary team to build agentic GenAI systems, foundation models, and optimization algorithms that accelerate Apple's chip design. We need an AI/ML Engineer who spans the modern AI stack, from high-level reasoning to low-level compute, to fundamentally shape how our silicon teams operate. This role is ideal for a hands-on technical leader who takes novel research to production deployment and thrives at the intersection of ambiguity, scale, and cutting-edge innovation.

Description
You will join a growing team of ML and software engineers developing state-of-the-art AI systems tailored for physical hardware design domains. In this role, you will have the opportunity to:

• Design and build advanced AI agents featuring multi-agent workflows, search and planning algorithms, self-reflection loops, and tool-calling architectures to automate chip design workflows.
• Train, adapt, and scale modern deep learning and GenAI architectures, including transformers, multimodal models, and hybrid generative backbones to solve complex domain-specific tasks.
• Solve challenging numerical optimization problems by writing high-performance custom primitives(e.g., CUDA, Triton, Metal) to make previously impossible experiments computationally viable.
• Partner with hardware and software teams across Apple, translating theoretical ideas and silicon design requirements into actionable engineering tasks.
  
  

Minimum Qualifications

• BSc, MSc, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or related fields.
• 5+ years of applied ML/AI industry experience deploying LLMs, foundation models, or complex ML systems in production.
• Strong mathematical foundation in linear algebra, probability and statistics.
• Track record as a rapid prototyper who defends hypotheses with empirical data and pivots instantly.
• Exceptional programming skills in Python and deep expertise in modern deep learning frameworks (e.g.,PyTorch, JAX).
• Excellent communication and collaboration skills to successfully bridge the hardware and software domains.
  
  

Preferred Qualifications

• Proficiency in C/C++ and GPU microarchitectures, with hands-on CUDA/Triton development experience.
• Experience building stateful, multi-turn agentic frameworks and complex execution flows.
• Track record of training large-scale models across distributed clusters (including exploration of non-attention architectures).
• Familiarity with Physical Design, EDA, or silicon design environments.
  
  

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Role Number: 200668375-0865