Inference Optimization Engineer; local​/edge runtime

Job Details

Job Description:

At Intel, our journey is to transform AI into something safer, more trustworthy, and respectful of human privacy by design. We believe transformative AI should have a positive impact on people—powerful in capability, yet honest about its limits and protective of the data and resources it touches. To get there, 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 (AI PC, edge, on‑prem, and beyond), keeping data private and token costs low, while powerful cloud models handle the hardest work: planning, reasoning, and complex problem‑solving. Today, neither approach can deliver this alone. Together, they give people real capability without compromise—data stays private, spend stays predictable, and energy use stays in check.

We're building intelligence that scales without sacrificing trust, cost, or the planet—because the future of AI should belong to the people it serves.

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

• 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 goes above and beyond just a paycheck. Whether you're looking to build your career, improve your health, or protect your wealth, we offer generous benefits to help you achieve your goals. Find out more about the benefits of working at Intel.

Shift 1 (United States of America)

US, California, Santa Clara

US, Arizona, Phoenix; US, California, Folsom; US, Oregon, Hillsboro

The Client Computing Group (CCG) is responsible for driving business strategy and product development for Intel's PC products and platforms, spanning form factors such as notebooks, desktops, 2 in 1s, all in ones. Working with our partners across the industry, we intend to deliver purposeful computing experiences that unlock people's potential—allowing each person to use our products to focus, create and connect in ways that matter most to them.

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…