Senior Instrumentation Engineer Jobs in Oregon (NOW HIRING)

As a Senior Engineer at e184, you will lead the design and development of software systems that integrate and control the experimental hardware enabling ex utero gestation. Your work will focus on building intuitive, reliable interfaces that unify perfusion systems, continuous imaging platforms, environmental controls, and monitoring infrastructure into a cohesive operational platform. This includes the control logic, feedback loops, and data infrastructure that keep automated perfusion, fluid management, and environmental systems running reliably over multi-week experiments, not just the interfaces researchers use to monitor them.

You will own software development from interface design through system integration and deployment, working closely with engineers, biologists, and hardware teams to translate complex experimental workflows into robust, user-centered control systems. This is a highly hands-on role for an engineer who enjoys bridging UI design, real-time control systems, and scientific experimentation in a fast-moving startup environment where software reliability directly impacts biological outcomes.

• Design unified control interfaces: Build the interfaces researchers use to monitor and control perfusion, imaging, and environmental systems from a single platform, on-site and remotely.

• Integrate and automate experimental hardware: Build software that communicates with pumps, sensors, imaging devices, and environmental controllers, and automates fluid, reagent, and waste management across the platform.

• Develop closed-loop perfusion control: Design control systems that regulate flow, pressure, oxygenation, and temperature against physiological targets, evolving toward predictive, adaptive optimization as experimental data accumulates.

• Develop real-time monitoring and data infrastructure: Build dashboards and the underlying data pipelines that capture, store, and surface sensor and experimental data from long-running runs.

• Implement experiment automation and scheduling: Design workflows that support automated protocols and reproducible system operation.

• Ensure system reliability and safety: Build fault detection, error handling, and recovery mechanisms that keep long-duration experiments stable.

• Collaborate across disciplines and establish software standards: Work with hardware engineers and scientists to translate experimental needs into software architecture, and contribute to code, documentation, and testing practices that scale.

• 5+ years of professional software engineering experience spanning UI/UX, real-time systems, and/or controls engineering

• Strong experience designing and implementing complex user interfaces for technical or scientific applications

• Proficiency in modern frontend frameworks (e.g., React, Vue, or similar)

• Experience interfacing software with hardware systems, sensors, or laboratory instrumentation

• Experience designing systems for real-time monitoring or device control

• Experience designing closed-loop or feedback control systems (e.g., PID, model-based, or adaptive control) for physical or biological processes

• Experience designing data acquisition pipelines and database infrastructure for continuous sensor or time-series data

• Strong understanding of software architecture and maintainable system design

• Ability to independently drive software projects from concept through deployment

• Strong collaboration and communication skills in interdisciplinary environments

• Comfort working in a fast-paced, iterative startup environment

• Experience developing software for laboratory automation, robotics, medical devices, or scientific instrumentation

• Experience integrating imaging systems or real-time data streams

• Familiarity with control systems, hardware communication protocols (e.g., serial, USB, TCP/IP, MQTT)

• Experience building data visualization dashboards for live experimental monitoring

• Experience designing safety-critical or long-running control systems

• Experience with model predictive control (MPC), adaptive control, or applied machine learning for process optimization or anomaly detection

• Experience with time-series databases (e.g., InfluxDB, TimescaleDB) for continuous experimental monitoring

• Prior startup or early-stage technology development experience