Principal Embedded AI Engineer for Reservoir Computing

The opportunity

At GEMESYS, you'll help shape the technological foundation of a company rethinking how intelligence is built in hardware. We're pioneering a brain-inspired computing paradigm and your expertise will be key in transforming this vision into scalable, real-world systems.  As a Principal Embedded AI Engineer for Reservoir Computing (RC), you'll work side by side with the founders to define how analog RC connects with embedded AI. This is a rare opportunity to take ownership at the intersection of AI, hardware, and systems design and to see your ideas come to life in real applications solving tangible customer challenges in the field.

Your role

As a Principal Embedded AI Engineer, you will be part of our extended founding team - the group of early experts who define the DNA of GEMESYS.
Within our technical team, you will be working side by side with the founders to shape the future of GEMESYS' core technology. You will have end-to-end ownership over the integration of our novel analog RC approach into the broader AI ecosystem, ensuring seamless accessibility for standard developer workflows. This means understanding and designing interfaces, frameworks, and developer experiences that bring our hardware innovation into the hands of AI practitioners.  
You will also drive the development and implementation of RC models for on-device training at the edge, contributing directly to the realization of our memristive reservoir platform. Your work will define how analog reservoir computing bridges the gap between neuromorphic hardware and embedded AI systems, setting technical and cultural foundations for GEMESYS' next growth phase.

• Design, implement, and optimize reservoir computing architectures suitable for embedded edge devices such as microcontrollers, FPGAs, or custom integrated chips
• Work closely with our Hardware and Simulation Engineers to integrate digital signal processing units for our analog memristive reservoir
• Benchmark, validate, and improve Edge AI performance in real-world embedded scenarios

What you bring

You are an innovative Embedded AI Engineer with a deep understanding in RC.
Essential:

• Master's (or PhD) degree in Computer Science, Mathematics, Physics, Electrical Engineering or a related field
• Deep expertise in Reservoir Computing and Recurrent Neural Network architectures
• Hands-on experience developing and deploying machine learning models and digital signal processing algorithms on embedded platforms such as microcontrollers, FPGAs, or edge SoCs
• Firsthand experience with Edge AI toolchains or deployment frameworks (e.g., TensorFlow Lite, ONNX Runtime, TVM, PyTorch Mobile, Edge Impulse)
• Proficient English communication skills, both written and spoken

• Experience with digital twin development or model-based engineering
• Experience in working with embedded toolchains and platform ecosystems, including familiarity with the developer community
• Background in hardware-related neuromorphic computing

Our offer

• A dynamic team with a shared mission, where everyone sticks together and is treated equally
• A company where everyone is working together closely in flat hierarchies
• We offer challenges every day and organic growth opportunities as the company scales
• 30 vacation days, flexible hours, corporate benefits, team events and hybrid solutions are a given