Base Salary
$190k - $280k/yr
Responsibilities
- Analyze the latest machine learning workloads and their performance implications.
- Contribute hardware and software features for next-generation inference accelerators.
- Stay updated on the latest research in ML architecture and algorithms.
- Collaborate with product, hardware design, compiler, inference server, and kernel teams.
- Create analytical models to project performance on current and future hardware.
- Propose new hardware/software features to enable or accelerate algorithms.
Requirements
- BSEE with 10+ years of industry experience or MSEE with 8+ years of experience.
- Strong knowledge in computer architecture, hardware/software co-design, and performance modeling.
- Programming fluency in C/C++ or Python.
- Experience in developing analytical performance models and architecture simulators.
- Research background with publications in top-tier architecture or machine learning venues is a plus.
- Self-motivated team player with strong collaboration skills.
About d-Matrix
d-Matrix is an AI infrastructure company building the next generation of inference computing for the era of generative and agentic AI. Founded in 2019, d-Matrix is rethinking AI inference from the ground up with a full-stack approach spanning silicon, systems, networking, and software. Its flagship products, including the Corsair™ inference platform and JetStream™ inference fabric, are purpose-built to deliver high-performance, low-latency, and energy-efficient AI inference at datacenter scale. The company has raised nearly $500 million from a global syndicate of leading venture capital firms, sovereign wealth funds, and strategic investors, including Playground Global, Bullhound Capital, M12 (Microsoft’s Venture Fund), SK hynix, Temasek, Qatar Investment Authority, and Singapore’s EDBI. The company’s most recent financing valued d-Matrix at approximately $2 billion. As AI shifts from training to inference, the demands on AI infrastructure are changing. d-Matrix is building the infrastructure required to power the next generation of real-time AI at scale.