Sr. Software Engineer

Architect and develop mission-critical software for a next-generation automated oligo synthesizer, enabling precise control of mechanical, fluidic, and electrical systems. You’ll own the full-stack software infrastructure, from low-level hardware communication (USB/SPI/serial) and embedded firmware integration to GUI design, data analysis tools, and database management — ensuring seamless operation for end-user scientists.

Own the Software Development Life Cycle (SDLC) for instrument control software: design, implement, debug, test, and deploy across Java/Python/C++ codebases.

Modernize and refactor legacy systems while ensuring backward compatibility with low level software + embedded firmware (custom PCB controller) and hardware messaging protocols.

Collaborate cross-functionally with engineers, scientists, and IT to translate user feedback into scalable software improvements (UI/UX, performance, reliability).

Develop data analysis pipelines (Python scripts, databases) and real-time monitoring tools for high-speed acquisition and process optimization.

Act as SME for software architecture: document APIs, mentor junior engineers, and align technical decisions with long-term product roadmaps.

Requires Bachelor’s degree in Computer Science, Computer Science & Engineering, Software Engineering, Electrical Engineering or a related field.

Technical Non-Negotiables: 6–8 years in full-stack software development for instrumentation/robotics/embedded systems.

Expertise in:

Java: core codebase, multi-threaded code and GUI desktop applications (JavaFX or Swing).

Java, Maven, NetBeans IDE and Mercurial.

Python: scripting, data analysis tools.

Low-level comms protocols: USB, Serial, SPI, I2C.

Microcontrollers/embedded firmware (hands-on debugging).

GUI development (e.g., Java FX, Swing, Qt, or other modern frameworks).

Proven leadership in architecting complex systems (design patterns, version control, continuous improvement (CI) / continuous development (CD)).

Soft Skills & Culture Fit:

“Product mindset”: Balance technical rigor with end-user needs (scientists’ workflows).

Communicate complex ideas clearly to both engineers and non-technical stakeholders.

Autonomy: Self-driven in ambiguous, R&D-paced environments.