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# AI Inference Framework Engineer

- **Company:** quadric, Inc
- **Location:** Burlingame, CA, United States
- **Experience:** Experienced
- **Salary:** $160,000 - $195,000
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Application Frameworks, C++ (Programming Language), Compilers, Nvidia CUDA, Cursor, Memory Management, Python (Programming Language), Open Source Technology, Tensorflow, Perplexity AI, Pytorch, Claude Code, AI Coding Agents, Backend, GGUF, Model Inference
- **Published:** October 26, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=f9a71e322cf9a144

## About the Role

* 3+ years building or extending ML frameworks or inference runtimes
 * BS/MS in CS/CE or related fields
 * Strong C/C++ and Python
 * Solid understanding of framework internals: tensor memory management, graph scheduling, operator dispatch
 * Solid understanding of AI compute performance analysis and optimization
 * Fluent with AI coding agents (e.g. Claude Code, Cursor), and interested in building agent-driven workflows for backend and model bring-up
Preferred

 * Experience writing an accelerator backend (CUDA, Metal, Vulkan, Hexagon or another NPU) for an open-source framework
 * Contribution to open-source frameworks (PyTorch, llama.cpp, vLLM)
 * Understanding of low level AI compute pipelines, CPU/NPU, DMA
 * Understanding of graph compilers and kernel fusion, If this role resonates with you, we encourage you to apply even if your experience does not perfectly match every qualification. We value potential, curiosity, and a willingness to learn just as much as direct experience.

## Description

* Implement framework backend interface, memory allocation, compute dispatch, registration and operators/kernels
 * Implement model compute pipelines including prefill, decode, batching, multi-sequence and multi-modal
 * Support important framework serving features including KV-cache management, paged attention, continuous batching, speculative decoding

Optimize framework performance and accuracy for GPNPU backend

 * Reduce host-device dispatch overhead, implement graph capture/replay, integrate efficient kernels, minimize latency
 * Support quantization formats such as GGUF (Q4/Q8), GPTQ/AWQ, int8/fp16
 * Validate accuracy against reference backends (perplexity, task benchmarks) and drive improvements