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Support SoC subsystem development, third-party IP integration, and power, performance, and area optimization for Quadric’s edge AI processor. Assist with low-power digital design, timing closure, architecture documentation, debugging, and front-end RTL development using SystemVerilog or Verilog while collaborating with SoC architects and hardware designers.

Own customer evaluations through design wins by porting and optimizing workloads on Quadric’s Chimera GPNPU. Develop custom kernels, profile performance, debug customer implementations, and guide model architecture tradeoffs. Build reusable SDK skills, porting playbooks, benchmark applications, and agentic FDE tooling. Collaborate with engineering and product teams to incorporate customer feedback into the roadmap.

Develop and extend neural network compiler passes targeting Quadric's GPNPU. Debug compilation issues by tracing generated C++ through compiler pipelines, optimize memory movement and hardware utilization, and contribute to IR transformations, code generation, testing, and developer tools. Collaborate with kernel, hardware, and data science teams while gaining hands-on experience in ML compilation and accelerator-aware optimization.

Define processor microarchitecture, implement RTL in SystemC or SystemVerilog, optimize power, performance, and area, and support timing closure through tapeout. The role owns major design blocks, debugs across the hardware/software stack, packages designs for customer releases, and incorporates customer feedback. Candidates need at least five years of CPU, GPU, or ASIC front-end design experience, strong computer architecture knowledge, digital low-power design expertise, and proficiency with relevant simulation and co-simulation tools.

Develop and optimize AI/LLM kernels and operators for Quadric’s edge AI platform. Profile performance, identify hardware and software bottlenecks, optimize C/C++ code, and contribute to compiler, runtime, and toolchain improvements. The intern will collaborate with senior engineers and gain hands-on experience with hardware-aware inference optimization and GPNPU architecture.

Lead Quadric’s marketing function from the ground up, defining strategy, brand narrative, product marketing, demand generation, PR, developer marketing, and events. Build and manage the marketing team, agencies, processes, budget, and analytics. Partner with product, engineering, business development, and executives on go-to-market initiatives, launches, positioning, sales enablement, communications, analyst relations, and industry presence.

Integrate Quadric’s GPNPU as a backend for llama.cpp, PyTorch, vLLM, and Optimum. Implement memory management, dispatch, operators, model pipelines, serving features, quantization, and performance optimizations. Validate accuracy against reference backends and collaborate with kernel, runtime, and compiler teams to bring up models and production features such as paged KV caching.

Deploys and optimizes AI models on Quadric’s GPNPU for vision, LLM, VLM, and VLA workloads. Responsibilities include profiling performance, writing and tuning kernels, selecting quantization strategies, debugging accuracy, optimizing compute graphs, and developing automated model deployment, tracing, visualization, pruning, sparsification, and quantization tools.

Develop foundational systems software for Quadric’s General-Purpose Neural Processing Unit, including instruction set simulators, loaders, assemblers, remote debugger infrastructure, drivers, and C++ host and device APIs. Collaborate with hardware and software teams on architecture decisions, performance optimization, binary execution, memory management, abstraction layers, and low-level toolchain development.

Design and integrate SoC subsystems, third-party IP, and processor architecture while optimizing power, performance, and area. Drive timing closure, low-power design, customer-ready releases, and cross-stack hardware/software debugging through tapeout.