Lead GenAI Engineer

Apple • Seattle, WA

8h

About The Position

Imagine what you could do here. At Apple, innovative ideas have a way of
becoming extraordinary products, services, and customer experiences very
quickly. Bring passion and dedication to your job and there's no telling what
you could accomplish! As part of Apple Cloud AI, we are building the next
generation of ML infrastructure that powers AI capabilities across Apple's
products and services. Our team tackles some of the most challenging problems in
the industry - optimizing LLM inference at massive scale, building distributed
training systems that push the boundaries of GPU and TPU utilization, and
architecting model serving platforms that deliver sub-millisecond latency for
real-time AI experiences. You'll work with cutting-edge technologies including
vLLM, Ray, TensorRT-LLM, TPU Infrastructure, and custom inference engines, while
shaping how foundation models are trained, fine-tuned, and deployed across
Apple's ecosystem. As a Lead GenAI/ML Engineer, you will architect
high-performance ML systems from the ground up - designing efficient KV-cache
strategies, implementing speculative decoding, optimizing tensor parallelism
across GPU and TPU clusters, and building the infrastructure that brings Apple's
most ambitious AI capabilities to life.

DESCRIPTION

This role requires translating cutting-edge ML research into production-ready
systems that meet the demanding requirements of Apple's ML workloads. You will
work closely with research teams to productionize new model architectures and
optimization techniques. We are looking for candidates who thrive at the
intersection of ML research and systems engineering - someone who can read a
paper on FlashAttention or PagedAttention and implement a production-grade
version, or who can profile a training job and identify opportunities to improve
GPU utilization from 40% to 80%.

Requirements

• 8+ years of experience in ML systems engineering, with at least 3 years focused on LLM/GenAI infrastructure
• Deep expertise in LLM inference optimization: KV-cache management, batching strategies, quantization, speculative decoding
• Strong proficiency in Python and C++/CUDA for performance-critical code
• Hands-on experience with inference frameworks: vLLM, TensorRT-LLM, Triton Inference Server, or equivalent
• Experience with distributed training at scale using frameworks like DeepSpeed, Megatron-LM, FSDP, or Ray Train
• Solid understanding of transformer architectures and attention mechanisms at the implementation level
• Experience optimizing ML workloads on NVIDIA GPUs (profiling, memory optimization, kernel tuning)
• Track record of taking ML systems from research/prototype to production at scale
• MS or PhD in Computer Science, Machine Learning, or equivalent practical experience

Nice To Haves

• Experience with TPU infrastructure (JAX/XLA, TPU training/serving optimization)
• Contributions to open-source ML infrastructure projects (vLLM, Ray, TensorRT-LLM, etc.)
• Experience with custom CUDA kernel development or Triton (OpenAI)
• Deep knowledge of model compression techniques: pruning, distillation, mixed-precision training
• Experience with multi-node training orchestration and fault tolerance
• Familiarity with emerging architectures: MoE models, linear attention variants, state-space models
• Experience building ML platforms serving high QPS with strict latency requirements

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level