The Role We are seeking a Systems Engineer with experience taking complex systems to production, integrating multi-domain hardware elements into robust, scalable products. This role sits at the heart of our Liquid Brain, where precision hardware, advanced firmware, and cloud based data analytics and intelligence converge to transform biophysical data collection into a seamless, automated process. This role is deeply hands-on: you’ll work across hardware, firmware, and software interfaces, driving clarity, testability, and refinement at every level. Your goal is to make the entire system greater than the sum of its parts — balancing speed and rigor, design and deployment, iteration and reliability. What you'll do Define and implement automated test frameworks and calibration protocols to ensure performance consistency across modules and builds. Collaborate with applications scientists, software and data science teams to align firmware interfaces (often Python-based) with system APIs and backend controls. Own triage, diagnostics, and version management, ensuring systems remain testable, traceable, and upgradeable. Work with external partners and suppliers to transition from bench-top prototypes to deployable, scalable hardware modules. Establish best practices for system validation, documentation, and traceability during assembly and deployment. Design and execute integration and endurance tests, tracking performance drift and identifying early signs of component degradation. Contribute to hardware system design reviews, ensuring manufacturability and serviceability. Support deployments and field testing of Liquid Brain systems with external partners and early customers. Who You Are You’re obsessed with the craft of making complex systems work beautifully — the kind of engineer who notices timing jitter in a motor trace, or a tiny lag between optical capture and fluidic trigger, and enjoys solving it elegantly. You balance precision with pragmatism, and can see both the micro and macro — from pin-level signals to full-system orchestration. You thrive in multidisciplinary environments where attention to detail is the difference between a lab experiment and a production-ready system. Important to Have Proven experience bringing a multi-component scientific or industrial system from prototype to scaled deployment. Strong working knowledge of at least two of the following: Optical systems (imaging, spectroscopy, or laser-based setups) Fluidic systems (pumps, valves, microfluidics, or reagent control) Electrical Engineering (Basic AC/DC power, signal processing) Scientific instruments (HPLC, spectrophotometers, or similar) Proficiency in Python for hardware control, data collection, and firmware-level diagnostics. Comfort operating in a startup environment, where flexibility, rapid iteration, and hands-on problem solving are key. Strong documentation discipline—able to generate wiring diagrams, test logs, and integration guides that scale across modules.