Apple
Engineering Manager, Private Cloud Compute Inference Routing
$226K–$382K
Seattle, Washington · Manager · Full-time · Posted Oct 23, 2026
- Experience
- Extensive software engineering experience; no specific number of years stated.
- Education
- Bachelor’s degree in an engineering discipline or equivalent professional experience.
About the job
Lead the teams and technical strategy building Private Cloud Compute inference routing and scheduling systems for Siri AI and Apple Intelligence, preserving privacy while improving latency, reliability, and fleet utilization.
What you'll do
- Lead recruitment, development, and management of engineers and engineering managers responsible for inference routing and request scheduling.
- Set technical strategy and oversee adaptive routing, gateway capabilities, capacity management, and reliability practices.
- Build roadmaps and coordinate delivery across inference, infrastructure, security, Siri AI, Apple Intelligence, and SRE teams.
What they ask for
- Extensive software engineering experience leading teams that build and operate large-scale distributed systems in production.
- Experience delivering API gateways, request routing, load balancing, scheduling, or traffic-management systems at scale; expertise in LLM inference serving.
- Record of shipping highly reliable systems, including incident management, on-call, and reliability practices.
- Server development background in Java, C/C++, Go, or Swift; ability to drive cross-team alignment; bachelor’s degree in an engineering discipline or equivalent professional experience.
Nice to have
- Leadership developing LLM inference serving or routing platforms; experience with inference gateways and their core behaviors.
- Experience with capacity-aware routing and accelerator utilization, privacy-preserving design, observability, or Kubernetes.
Pay
$225,600 and $381,600 (base salary)
Tools
Java, C/C++, Go, Swift, Kubernetes, llm-d, NVIDIA Dynamo, vLLM
AI focus
Lead routing and scheduling for LLM inference, including cache-aware placement, KV-cache placement and reuse, and prefill/decode disaggregation, within PCC’s privacy architecture.
Model-written summary · the original posting is authoritative