Materials Data Engineer – Active Clearance Required

LLNL•Livermore, CA

4d•Hybrid

About The Position

We are seeking a Materials Data Engineer to join our team. In this role, you will work under the guidance of principle investigators to design data and analysis pipelines to support the Laboratory's AI-initiatives. This includes but not limited to sparse datasets, manufacturing, and high-throughput experimentation. As part of the Materials Engineering Division (MED) within the Engineering Directorate, you will contribute to advancing strategies for national security and support DOE’s Genesis Mission. This position provides an excellent opportunity to work at the forefront of science, automation, and machine learning. This position offers a hybrid schedule, blending in-person and virtual presence. You will have the flexibility to work from home one or more days per week.

Requirements

This position requires an active Department of Energy (DOE) Q-level clearance or active Top-Secret clearance issued by another U.S. government agency at the time of hire.
Bachelor's degree in computer engineering, Electrical Engineering, Mechatronics, or a related technical field, or equivalent combination of education and relevant experience.
Experience developing software for data acquisition and processing, which includes tabular and time series data, and ingestion into databases such as MongoDB.
Fundamental understanding of networking and IoT communication for remote monitoring and control, including designing secure, low latency networks that connect PLCs, robots, sensors, edge devices, and database backed services using protocols such as TCP/IP and RESTful APIs.
Ability to work both independently and collaboratively in a multidisciplinary and dynamic team environment, utilizing experience and fundamental knowledge of experimental design, techniques, and execution.
Sufficient organizational, verbal, and written communication skills to effectively collaborate in a team environment and present and explain technical information to a variety of audiences.
Demonstrated experience designing and implementing safety-critical control systems (safety relays for laser systems, EMO circuits, door interlocks, remote interlock I/O, watchdog timers, and PLC‑based Safety Instrumented Systems) and standards-compliant architectures for high-energy or laser systems.

Nice To Haves

Advanced degree in Computer Engineering, Electrical Engineering, Mechatronics, or a related technical field.
Proficiency in computational data analysis and scientific programming, including experience applying Python stacks (NumPy, Pandas, matplotlib, ML frameworks, MongoDB, and automated CSV/Parquet pipelines) and schema/ontology design and visualization dashboards.
Hands-on experience developing and tuning closed-loop control systems (e.g., PID) in PLCs, embedded firmware, or software, with additional skills in C/C++, LabVIEW, MATLAB, and collaborative development tools (e.g., Git).

Responsibilities

Design and implement data processing and analysis pipelines that are scalable and AI-ready for high-throughput laboratory experimental systems to support end-to-end and hardware-software workflows and interface with enterprise data lake platforms such as MongoDB.
Develop, deploy, and maintain application‑layer and AI‑assisted control software that interfaces with industrial and laboratory hardware — including PLCs, robotic controllers, motion systems, sensors/actuators, machine vision systems, and HMI/SCADA platforms — to provide robust, low‑latency monitoring and control of experiments.
Leverage AI and statistical methods to enable early detection of off‑normal or unsafe conditions, scaling from benchtop experiments to large integrated facilities and HPC platforms.
Specify, integrate, and validate hardware–software safety and interlock functions in close collaboration with scientists, engineers, and technicians.
Ensure that control logic, AI‑assisted decision components, data models, test plans, and configuration‑managed documentation meet LLNL, DOE, and applicable SIL/compliance, software quality assurance (SQA), and cyber‑security requirements while supporting rigorous change control and regression testing over the system life cycle.
Participate in hardware design reviews and create reports.
Perform other duties as assigned.