Sr. Inference Optimization Engineer (local / edge runtime)

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

to find candidates for this position which is frequently available

at this time. If you would be interested in this position should it

Minimum qualifications are required to be initially considered for this position.

Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates.

You must possess the minimum qualifications to be initially considered for this position.

Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates.

- 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

- Experience with GPU / accelerator programming (Vulkan, CUDA, SYCL, Metal) or SIMD / CPU kernels

- Familiarity with quantization formats and their quality trade-offs

Requirements listed would be obtained through a combination of industry relevant job experience, internship experiences and or schoolwork/classes/research.

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. Go to Intel Benefits | Intel Careers for details of benefits available to you.

Intel reserves the right to modify, change or discontinue benefit plans at any time in its sole discretion.

Additional Locations: US, Arizona, Phoenix, US, California, Folsom, US, Oregon, Hillsboro

Annual Salary Range for jobs which could be performed in the US: $195,200.00-361,200.00 USD

Work Model for this Role This role will be eligible for our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site. * Job posting details (such as work model, location or time type) are subject to change.