Automation Engineer

About the role

Samsung SDI Research America (SDIRA), based in Cambridge, MA, is one of several newly established global battery R&D centers created to foster cutting edge research and pioneer the development of Samsung's next-generation cell technologies. The location is close to world-renowned universities and start-up companies, enabling unique collaborative research within a strong North American ecosystem of battery development expertise.

Responsibilities

• Develop, optimize, and maintain control software that coordinates sensors, actuators, and a wide range of laboratory equipment
• Design control logics and algorithms for high-throughput experimentation
• Build function-specific devices/tools to address challenges and inefficiencies in automated workflows
• Identify technical requirements and risks associated with the buildout of the high-throughput experiment platform, and propose actionable solutions/strategies
• Collaborate with a multidisciplinary team of scientists and engineers in a fast-moving environment
• Produce concise technical reports and presentations for efficient communication of project status

Required qualifications

• Bachelor's degree in Mechanical, Electrical, Computer Engineering, Mechatronics or a related field
• Minimum of 2 years of relevant experience (including advanced degrees in relevant fields)
• Proficiency in Python, C/C++ or similar programming languages
• Proficiency in version control tools (e.g., SVN/Git/GitLab/GitHub)
• Experience in microcontroller development (e.g., Arduino, Raspberry Pi)
• Experience in prototyping custom devices/mechanisms and sensor/motor integration
• Familiarity with communication protocols (e.g., TCP/IP, RS-232)

Preferred qualifications

• Master's degree in Mechanical, Electrical, Computer Engineering, Mechatronics or a related field
• Experience with CAD and 3D printing
• Familiarity with materials development R&D
• Familiarity with database platforms (e.g., MongoDB, influxDB, questDB)
• Experience implementing ML and Bayesian optimization frameworks (e.g., PyTorch, TensorFlow, BoTorch)