Join d-Matrix and help build the future of AI inference. We’ve pioneered a memory-centric architecture that brings compute directly into memory, making AI faster, more energy efficient, and accessible on a global scale. From chips and software to rack-scale systems, our team is tackling some of AI infrastructure’s hardest challenges — and shipping breakthrough technology today. If you want to create what comes next and help make infinite inference possible, we’d love to hear from you.

Our culture is built on respect, collaboration, and direct communication. We value humility, we believe our different perspectives lead to better solutions, and we’re looking for people who are driven by execution and energized by hard problems.

D-Matrix Frontier Group sits at the leading edge of what’s possible with LLM inference on heterogeneous hardware. Our charter spans the full stack: from pathfinding emerging use cases and novel deployment patterns to deep optimization of inference kernels to building proof-of-concept systems that showcase D-Matrix’s unique computational fabric. We are an applied research and engineering team that moves fast, ships real systems, and works directly with product and hardware teams to shape the roadmap.

We build the tools, runtimes, and frameworks that let frontier AI models run efficiently and cost-effectively across heterogeneous deployments combining D-Matrix silicon with CPUs, GPUs, and custom accelerators. Our work powers everything from benchmarking and evaluation pipelines to production-grade inference serving.

This Role

We are hiring end-to-end inference engineers who are comfortable going from a novel research idea to a deployed, optimized system. You will work at every layer of the inference stack from kernel-level optimization to distributed orchestration to high-level serving APIs.

This role could be a great match for you if you

Have deep intuition for modern generative AI architectures and how to squeeze performance out of them at inference time.

Are familiar with the internals of open-source inference frameworks (vLLM, SGLang, TensorRT-LLM, etc.) and can extend or replace them when needed.

Enjoy pathfinding new use cases — exploring heterogeneous deployment topologies and building early-stage POCs that prove out new ideas.

Are results-oriented with a strong bias toward action; you own problems end-to-end from prototype to optimization to handoff.

Are energized by working at the intersection of novel hardware and frontier models and want your work to directly influence how next-generation AI silicon is used.

Value clear communication and thrive in a small, high-ownership team environment.

Responsibilities

Identify and prototype emerging LLM inference use cases suited to heterogeneous hardware deployments.

Build compelling proof-of-concept systems that demonstrate D-Matrix capabilities to customers, partners, and internal stakeholders.

Develop and tune custom kernels and operator-level optimizations to maximize throughput and minimize latency.

Drive quantization, sparsity, and batching strategies tailored to the D-Matrix computational model.

Build and maintain inference runtimes, serving frameworks, and evaluation tooling.

Contribute to distributed inference systems: tensor/pipeline parallelism, disaggregated prefill/decode, KV-cache management.

Work closely with hardware architects to provide firmware and compiler teams with actionable inference workload insights.

Partner with product and business development to translate POCs into customer-facing demonstrations.

Contribute to technical publications, whitepapers, and open-source projects that advance D-Matrix visibility.

Required Qualifications

Bachelor’s degree in Computer Science, Electrical Engineering, or a related field, and 10+ years of relevant engineering experience or equivalent demonstrated experience.

Master’s or PhD in Computer Science, Electrical Engineering, or a related field preferred, with 6+ years of relevant industry experience.

Strong proficiency in Python and C/C++.