GenAI Research Engineer

Quick Facts

• Role: GenAI Research Engineer

Description

Make generative AI models dramatically faster, smaller, and cheaper to run on-device and on server. Work across the efficiency stack, including inference optimizations for production diffusion models, hardware-aware architecture design, and end-to-end ownership from prototyping and evaluation to deployment.

Responsibilities

• Develop and deploy optimization algorithms to speed up inference of production diffusion models (PTQ, QAT, kernel fusion, model distillation).

• Design hardware-aware architectures that fit within compute and memory limits with minimal loss of quality.

• Own the end-to-end process from prototyping, evaluation, and deployment into the Apple ecosystem.

Requirements

• M.S. or PhD in Computer Science or a related field (e.g., Electrical Engineering, Robotics, Statistics, Applied Mathematics) or equivalent experience.

• Deep, demonstrated expertise in at least one of:

  • Quantization (low-bit and mixed-precision methods, quantization-aware training).

  • Efficient architecture design, structured pruning, or neural architecture search for GenAI/Vision applications.

  • Hands-on experience training and fine-tuning diffusion or other large generative models in PyTorch, JAX, or an equivalent framework.

• Ability to reason about efficiency beyond FLOPs (memory, latency, numerical precision) and eagerness to learn hardware-specific constraints.

• Excellent cross-functional collaboration skills to explain model trade-offs, hardware constraints, and integration needs to partners across writing and in-person discussions.

Benefits

• Opportunity to advance and ship state-of-the-art GenAI efficiency technology with broad impact through on-device and server deployment.