Embedded Software Engineer

Company Description

PredictiveIQ specializes in advanced Physics Informed AI solutions, streamlining engineering design, boosting asset performance, and optimizing fleet operations for cyber-physical systems. Utilizing cutting-edge neural architectures and machine learning techniques, our data-efficient technology delivers predictive capabilities in real-time, across embedded, edge, and cloud deployments. A primary application of this technology includes Predictive Maintenance Digital Twins, catering to industries such as Defense, Transportation, Aerospace, Mining, Automotive, and more. Our mission is to revolutionize the performance of cyber-physical systems through innovation and data-driven insights. Learn more at www.predictiveiq.com.

Role Description

PredictiveIQ is seeking an Embedded Software Engineer to lead the software development work required to deploy physics-informed AI and health-monitoring algorithms into an operational embedded environment. This role is centered on taking ONNX-based models and building the production-grade C/C++ software layer within the vehicles Integrated Vehicle Health Management System (IVHMS) needed to execute them within ground combat vehicle.

The ideal candidate is first and foremost a software builder: someone who can translate model interfaces and runtime requirements into robust embedded code, collaborate effectively with subcontractors and systems stakeholders, and support integration, debug, and verification activities without losing focus on delivery.

This is a full-time remote role for an Embedded Software Engineer. The role involves designing, developing, and optimizing robust embedded software solutions, debugging and troubleshooting code, and collaborating with a cross-functional team to ensure software meets performance requirements. The engineer will work closely with other departments and clients to ensure seamless integration with the broader system architecture.

Key Responsibilities

• Develop embedded C/C++ software to deploy PredictiveIQ ONNX models within the ground combat vehicle common-compute / IVHMS environment.
• Design and implement the library, interfaces, and runtime software needed to execute model inference in an embedded or edge-deployed setting.
• Work with internal engineers and subcontractor teams to define software interfaces, data flows, integration assumptions, and acceptance criteria.
• Translate model inputs, outputs, and performance constraints into production-ready embedded software structures.
• Support integration of deployed algorithms into the broader VHMS architecture and assist in troubleshooting software and interface issues.
• Contribute to software packaging, versioning, documentation, and delivery artifacts needed for compliant program execution.
• Assist with test readiness, defect triage, and verification support for the deployed software stack.
• Collaborate across software, AI/ML, systems, and program teams to ensure the delivered implementation is technically sound and integration-ready.

Required Qualifications

• Bachelor’s or Master’s degree in computer engineering, computer science, electrical engineering, or a related technical field.
• 3+ years of experience in embedded software development, defense software, automotive software, or closely related real-time / edge-compute environments.
• Strong proficiency in C and C++, with the ability to develop maintainable, integration-ready embedded software.
• Experience working with embedded Linux or similar runtime environments.
• Experience translating technical requirements into software interfaces, modules, and deployable code.
• Comfort working across disciplines, including AI/ML, software integration, systems engineering, and program execution.
• Strong written and verbal communication skills for technical coordination and documentation.

Preferred Qualifications

• Experience in defense ground vehicle systems, military platforms, or adjacent embedded defense programs.
• Automotive embedded software experience, especially where software is integrated with sensors, controls, or vehicle health data.
• Familiarity with ONNX model deployment, inference runtimes, or AI/ML algorithm integration into production software.
• Experience with Python for tooling, prototyping, or model handoff workflows.
• Exposure to vehicle diagnostics, IVHM / VHMS, condition-based maintenance, or sustainment-oriented systems.
• Familiarity with MBSE / SysML / Cameo, interface control practices, or requirements traceability.
• Experience supporting integration, verification, and qualification activities in regulated or defense environments.