At GEMESYS, we’re building next-generation edge AI hardware designed to learn directly on the device. As a Senior Analog-/Mixed-Signal Design Engineer, you’ll own critical parts of the IC design stack and work directly with the founding team to take this technology from lab to product. Your designs will sit at the intersection of AI, hardware, and systems engineering, solving real customer challenges across industries like industrial AI, healthcare, energy, robotics, and space.
This is not an incremental improvement on existing product lines. It’s a fundamentally new architecture, and your circuit designs will define what it can do.
This role covers the full IC design stack, from concept to successful silicon implementation, including top-level integration, post-layout verification, and final design sign-off. Experience with PCB design, system integration, and chip testing is a strong plus.
You are an innovative IC Design Engineer with a deep understanding of advanced analog design theory and a strong background in analog/mixed-signal circuit design, built around low-power and low-leakage circuits.
Essential
Preferred:

Current hardware for artificial intelligence is inefficient. For today's supercomputer center, it takes a long time, huge datasets and the energy of a whole power plant to train a high-end AI model, resulting in high costs and unsustainability. The problem is rooted in the fundamental architecture of today’s digital hardware itself, since it has nothing in common with the way the human brain works. GEMESYS Technologies offers an analog chip design based on the same information-processing mechanisms as the human brain. This enables AI hardware vendors to distribute a novel chip, that trains neural networks 20,000 times more energy-efficient than current technology. It not only significantly reduces the cost, time and amount of data required to train a neural network, but also increases overall quality as well as performance. Its small size and high energy efficiency allows it to be embedded in nearly every device, enabling decentralized on the edge training, data processing and decision making.