Applied Super Intelligence Engineer, Physical Systems

LLNL•Livermore, CA

17h•$121,830 - $185,544•Hybrid

About The Position

Lawrence Livermore National Laboratory is seeking an Applied Super Intelligence (SI) Engineer, Physical Systems, to build software that enables SI agents and multimodal models to interact with real-world equipment through an existing hardware integration stack. The position focuses on applied SI and perception workflows that transform operational data and equipment interfaces into safe, usable SI-assisted systems. It is in the Material Engineering Division (MED) within the Engineering Directorate and offers a hybrid schedule, with the flexibility to work from home one or more days per week. The position may be filled at either level based on the candidate’s knowledge and related experience. At the higher level, additional responsibilities include designing and evaluating agent workflows across multiple system components, input modalities, and operational steps; improving reusable evaluation and failure-analysis methods; and coordinating with embedded and SI platform engineers while providing informal guidance to less-experienced contributors.

Requirements

Ability to secure and maintain a U.S. DOE Q-level security clearance, which requires U.S. citizenship.
Bachelor’s degree in computer science, software engineering, robotics, or a related field, or an equivalent combination of education and related experience.
Proficiency in Python or a comparable programming language for building applications, prototypes, or internal tools in collaboration with software and systems engineers.
Experience developing LLM, multimodal, agent-based, or tool-calling applications through coursework, projects, research, internships, or professional work.
Experience designing, implementing, testing, and debugging tool-using SI workflows connecting models to APIs, services, data sources, or operational systems, including structured tool or API contracts such as JSON Schema or OpenAPI, error handling, logging, and evaluation.
Experience working with real-world data from sensors, cameras, devices, or operational systems.
Familiarity with sound software development practices, including version control, automated testing, and collaborative code review.
At the SES.2 level, ability to independently develop and evaluate multicomponent agent, multimodal, or tool-calling workflows for operational use.
At the SES.2 level, ability to scope and debug moderately complex workflow failures involving perception, tool use, system integration, and operational constraints.

Nice To Haves

Exposure to robotics workflows, ROS, or ROS 2.
Experience with vision-language models, vision-language-action models, or multimodal perception pipelines.
Familiarity with simulation, digital twins, or hardware-in-the-loop testing.
Familiarity with workflow orchestration, agent tracing or evaluation systems, prompt or configuration versioning, replay-based testing, or event and telemetry systems such as MQTT or NATS.

Responsibilities

Implement agent-facing tool definitions and task workflows using documented equipment interfaces and platform services.
Develop multimodal perception and decision-support workflows using camera, telemetry, and process data.
Improve prompt logic, tool use, evaluation methods, and workflow reliability for physical-world SI applications.
Translate operational tasks into practical SI-assisted workflows that are usable, testable, and maintainable.
Work with embedded, platform, and deployment engineers in a controlled environment to define safe action boundaries, approval points, and fallback behaviors using established external safety and security systems.
Evaluate and debug applied SI workflows through comprehensive logging, iterative refinement, and disciplined engineering practices.
Perform other duties as assigned.
At the SES.2 level, design and evaluate agent workflows combining perception, tool use, and equipment actions across multiple system components, input modalities, and operational steps.
At the SES.2 level, improve reusable evaluation harnesses, guardrails, and failure analysis for SI-assisted workflows, using results to guide technical decisions.
At the SES.2 level, coordinate with embedded and SI platform engineers to turn device capabilities, operational requirements, and workflow evaluation results into reusable agent workflows, and provide informal guidance to less-experienced contributors.