Lead Software Engineer
Role Overview
- As an experienced Lead Engineer with expertise in flight control systems and companion compute platforms, your leading role will be in designing, implementing, and maintaining software solutions across NXP-based PX4 flight controllers and Nvidia-based and Qualcomm-based mission computers. You will optimize solutions for reliability, safety, and performance in complex UAV operations.
- This position requires a deep understanding of real-time embedded systems, proficiency in PX4 autopilot software, and the ability to integrate high-performance applications on NVIDIA& Qualcomm IQ9 platforms. As a leader in our UAV development projects, you will play a key role in shaping our autonomy stack, ensuring scalability, safety, and secure communication across the system.
- Lead engineer will take ownership in developing SW features and productization of the TII Secure Software Platform for large-scale defense UxV (Unmanned Vehicle) mission computer applications.
Functional Activities
Autonomy Software Development:
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Integrate robotics applications on NVIDIA & Qualcomm IQ9 platforms for perception, planning, and navigation.
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Develop and integrate ROS2-based nodes and middleware to support modular autonomy stacks.
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Optimize GPU-based AI/ML workloads for real-time inference, sensor fusion, and decision-making.
System Integration and Middleware:
- Experience implementing secure application-level network protocols (NATS, ACL, DDS, etc.) between robotic platforms and Ground Control Station
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applications, including message exchange, authentication, encryption, telemetry, command/control flows, and secure communication handling.
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Develop tools and APIs for efficient data exchange across mission-critical components.
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Support multi-sensor integration (camera, LiDAR, GNSS, IMU) with ROS2 middleware.
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Has basic understanding on Flight Controller devices and PX4-based flight-control platforms, including real-time operation, MAVLink/UDP communication, telemetry, command/control flows, parameter management, sensor/peripheral interfaces, and integration with NVIDIA-based Mission Computer software.
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Ability to work with Flight Controller hardware, FPGA, security, and systems teams to validate end-to-end integration across Mission Computer, Flight Controller, Ground Control Station, and unmanned vehicle platforms.
Security and Reliability:
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Design and enforce cybersecurity measures for UAV autonomy software (secure boot, encrypted comms, sandboxing).
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Implement identity management and access control for mission software deployments.
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Conduct threat modeling, vulnerability assessments, and apply secure coding practices.
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Ensure compliance with aerospace cybersecurity frameworks (ISO 27001, DO-326A).
Automation and DevOps:
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Build CI/CD pipelines for deployment of ROS2 and AI workloads on NVIDIA Jetson and Qualcomm IQ9.
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Use containerization (Docker) and orchestration tools for reproducible autonomous flight stacks.
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Apply understanding of MATLAB and related engineering tools for automation, test scripting, data analysis, model-based workflows, validation support, and manufacturing procedure automation.
Engineering Process Optimization:
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Implement telemetry, logging, and diagnostics for real-time system monitoring.
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Continuously optimize software for GPU utilization, latency reduction, and energy efficiency.
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Define and enforce best practices in software development lifecycle (SDLC) for autonomy stack.
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Standardize coding guidelines, documentation, and review processes to ensure maintainability and quality.
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Define KPIs (e.g., deployment cycle time, test coverage, system uptime, security incidents) to track engineering efficiency.
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Encourage reuse of components and frameworks across projects to reduce duplication.
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Organize regular code reviews, technical retrospectives, and knowledge-sharing sessions.
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Sprint planning, backlog grooming, and daily stand-ups.
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Automated testing, HITL/SITL, code review, and release management.
- Metrics tracking, velocity, and continuous improvement of the engineering process.
Industry / Domain
Defense Autonomous Systems / Secure Mission Computing / Unmanned Vehicles (UxV)
Necessary Knowledge and Experience
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10+ years of experience in robotics, autonomous systems, or safety-critical UxV platform development, preferably in defense or aerospace environments.
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Proficiency in real-time embedded systems and Python for automation, AI/ML workflows, testing, and data pipelines.
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Strong experience with ROS2, XRCE-DDS, MAVLink, and modular autonomy stack architecture on NVIDIA Jetson and Qualcomm-based mission computer platforms.
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Hands-on experience with Qualcomm SoC platform bring-up, including BSP integration, Linux/Yocto environments, boot chain validation, peripheral enablement, driver integration, and platform debugging.
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Understanding of Qualcomm security features, including secure boot, hardware root of trust, TEE, key provisioning, encrypted storage, anti-rollback, secure debug control, and lifecycle-state enforcement.
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Experience with production security workflows, including fuse burning, root-of-trust provisioning, device identity, factory flashing, secure provisioning, and manufacturing test procedures.
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Hands-on experience with GPU, DSP, NPU, or AI accelerator-based workloads for sensor fusion, real-time inference, and SWaP-constrained AI/ML deployment.
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Experience implementing embedded cybersecurity measures, including encrypted communications, sandboxing, access control, threat modeling, secure OTA, and secure coding practices.
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Familiarity with ISO 27001, DO-326A, DevOps/CI/CD, Git, Docker, Kubernetes, automated testing, and cross-functional integration.
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Strong English communication skills and ability to work with software, hardware, security, manufacturing, QA, and systems teams under pressure.
Education and Certification Requirements
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Experience in UAV or aerospace autonomy projects.
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Contributions to the ROS2 or NVIDIA Jetson open-source community.
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Knowledge of ML, SLAM, or multi-agent coordination.
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Familiarity with standards (ISO 27001, DO-178C, DO-326A, IEC 61508).
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Proven track record of bringing autonomous systems from concept to product, including field deployment and user adoption.
