Senior Edge Inference Optimization Engineer

Intel; Phoenix (AZ); Hybrid; USD 195,200 - 361,200; Full time

Benefits offered by this job

Stock bonuses

Health insurance

Retirement plan

Paid vacation

Job summary

Intel is seeking a senior software engineer to optimize AI inference for hybrid US environments. You will advance local and edge inference engines (llama.cpp, vLLM) on PC/edge hardware, focusing on latency, memory, and energy efficiency.

Collaborating with teams, you will push quantization, KV cache strategies, and CPU/GPU tuning to deliver strong performance while upholding Intel's commitment to trusted AI and privacy by design.

Qualifications

• BS/MS in CS, EE, Math or related STEM field.
• 8+ years software development background.
• Strong in C++ and/or Python; comfortable reading systems-level code.
• Experience with LLM inference. (attention, KV cache, decoding)
• Experience profiling and optimizing real performance problems (CPU or GPU) and can prove the speedup
• Linux, build systems, and low-level debugging expertise.

Responsibilities

• Profile and optimize local inference (llama.cpp-vulkan and vLLM) for latency, throughput, and memory on edge hardware
• Tune KV cache, continuous batching, and scheduling for interactive agent workloads
• Drive quantization strategy (GGUF / AWQ / GPTQ) and validate quality impact with the Post-Training team
• Cut CPU overhead and improve engine startup, model load, and lifecycle (start / stop / health)
• Benchmark across hardware tiers and publish honest performance comparisons
• Upstream fixes and patches to open-source engines where it helps us

Skills

C++

Python

LLM inference

Profiling

Linux

Low-level debugging

Education

Bachelor's or Master's in CS/EE/Math or related STEM

Tools

llama.cpp

vLLM

ggml

Vulkan

CUDA

SYCL

oneAPI

Metal

Job description

Intel is seeking a senior software engineer to optimize AI inference for hybrid US environments. You will advance local and edge inference engines (llama.cpp, vLLM) on PC/edge hardware, focusing on latency, memory, and energy efficiency.

Collaborating with teams, you will push quantization, KV cache strategies, and CPU/GPU tuning to deliver strong performance while upholding Intel's commitment to trusted AI and privacy by design.