Inference Optimization Engineer; local​/edge runtime

Overview

At Intel, our mission is to transform AI into something safer, more trustworthy, and respectful of human privacy by design. We build agentic AI that combines the best of local and cloud intelligence—private, affordable, and sustainable by design. Small efficient models run directly on the user’s machine, keeping data private and token costs low, while powerful cloud models handle the hardest work: planning, reasoning, and complex problem-solving.

Together they give people real capability without compromise—data stays private, spend stays predictable, and energy use stays in check.

Make models fast on the hardware people actually own. You optimize inference engines (llama.cpp, vLLM) for constrained local and edge environments — GPU/iGPUs, Vulkan backends — not datacenter H100 environment, mostly PC/edge. KV cache, batching, quantization, scheduling, and CPU‑overhead reduction are your daily tools. This is the rare skill that makes a hybrid, low‑cost agent product viable.

• 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

• Understanding the internals of modern inference engines and where the milliseconds actually go
• Hardware‑aware optimization across iGPU / CPU paths (Vulkan, SYCL, oneAPI, CUDA where relevant)
• The quality‑vs‑speed‑vs‑memory trade‑space for small models
• Interest in local / edge AI and squeezing hardware

• BS/MS in CS, EE, Math or related STEM field
• 5+ years software development background
• Strong in C++ and/or Python; comfortable reading systems‑level code
• Understands how LLM inference works (attention, KV cache, decoding)
• Has profiled and optimized real performance problems (CPU or GPU) and can prove the speedup
• Linux, build systems, and low‑level debugging expertise

• Hands‑on with llama.cpp, vLLM, ggml, or similar engines
• Experience with GPU / accelerator programming (Vulkan, CUDA, SYCL, Metal) or SIMD / CPU kernels
• Familiarity with quantization formats and their quality trade‑offs
• Open‑source contributions to inference engines

Our total rewards package includes competitive pay, stock bonuses, health benefits, retirement plans, and vacation. Find out more about the benefits of working at Intel in the dedicated benefits section.

All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.

$ – $ USD (US locations)

This role will be eligible for a hybrid work model which allows employees to split their time between working on‑site at their assigned Intel site and off‑site.