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Maritime Digital Production (MDP) is the software and digital systems function within Anduril's Heavy Metal division. We build and deploy the full technology stack that powers Anduril's shipbuilding factories: the data infrastructure that makes every machine and sensor visible in real time, the manufacturing execution system (ArsenalOS) that workers and planners use every shift, the scheduling engine that replans production in minutes instead of days, and the AI systems that eliminate manual toil from both the shop floor and business operations.
MDP operates at the boundary between Operational Technology and Information Technology. Our systems live where factory-floor machines, edge compute, and OT networks meet enterprise platforms and cloud infrastructure. We incubate solutions close to the production line, validate them with real operators building real hardware, harden them for reliability and security, and then scale them across multiple sites. The environment is fast, physical, and consequential. When our systems go down, production stops. The output of our work is not a dashboard: it is a ship.
This is not a support function. It is a strategic investment by Anduril in the premise that digitizing the manufacturing lifecycle end-to-end, from engineering definition through scheduling through execution through field feedback, is how Heavy Metal will out-build, out-adapt, and out-scale the traditional defense industrial base. MDP is scaling from a founding team to 70+ engineers across multiple U.S. sites. You will be joining early, working on hard problems with real operational stakes, and shaping how manufacturing software is built at Anduril from the ground up.

As a Senior IoT Engineer on the Applied Intelligence initiative, you will build and deploy the connected sensor infrastructure and edge intelligence systems that power ML pipelines for factory sensing, document processing, and drawing conversion. You will develop IoT-enabled data collection systems that feed computer vision, NLP, and RAG-enabled AI workflows, creating intelligent sensing capabilities across the manufacturing environment.

• Design, build, and deploy IoT sensor networks and edge devices that capture real-time factory data for ML pipelines including computer vision systems, document processing, and production monitoring.
• Develop edge intelligence systems that run ML inference locally on IoT devices and edge compute for real-time factory sensing, quality inspection, and anomaly detection.
• Partner with manufacturing engineers and factory operators to understand production workflows and translate them into IoT sensing and data collection requirements.
• Write production-quality code with comprehensive tests, participating in code review and architectural discussions.
• Implement secure, scalable IoT communication architectures using industrial protocols (MQTT, AMQP, OPC-UA, Sparkplug B) to transmit sensor data to cloud and edge ML pipelines.
• Build data ingestion pipelines that connect IoT sensors, cameras, and edge devices to ML model serving infrastructure for computer vision, document extraction, and NLP applications.
• Design and implement edge compute architectures that support on-device ML inference, local data processing, and intermittent connectivity scenarios in factory environments.
• Deploy and operate your systems in factory environments, including edge compute clusters and OT networks.
• Develop firmware and embedded software for microcontrollers, single-board computers (Raspberry Pi, NVIDIA Jetson), and industrial IoT gateways.
• Implement cybersecurity best practices including device authentication, encryption, secure boot, and PKI for connected factory devices.
• Leverage AI tooling (coding assistants, automation) in your development workflow and contribute to team engineering practices.
• Join an on-call rotation supporting production factory systems.

• 5+ years of experience in a software engineering role building production systems, ideally in a fast-paced environment.
• Deep expertise in IoT systems engineering, including embedded development, edge computing, and IoT cloud architectures.
• Strong technical fluency in modern software architectures, APIs, distributed systems, CI/CD, and cloud or edge infrastructure.
• Proficiency in programming languages for embedded and IoT development (C, C++, Python, JavaScript/Node.js).
• Hands-on experience with microcontrollers and single-board computers (Arduino, Raspberry Pi, NVIDIA Jetson, or industrial equivalents).
• Deep understanding of IoT communication protocols (MQTT, CoAP, HTTP/HTTPS) and wireless technologies (Wi-Fi, Bluetooth, LoRa, 5G, Zigbee).
• Experience with cloud IoT platforms (AWS IoT Core, Azure IoT Hub, Google Cloud IoT) and deploying edge-to-cloud data pipelines.
• Experience building systems that must operate reliably under real-world operational constraints (high availability, low latency, or constrained environments).
• Knowledge of cybersecurity for IoT including encryption, secure authentication, device PKI, and vulnerability mitigation.
• Excellent written and verbal communication skills, with the ability to collaborate across engineering, manufacturing, and operations teams.
• Degree in Computer Science, Electrical Engineering, Information Systems, Engineering, or related technical field, or equivalent practical experience.
• U.S. Person status is required as this position needs to access export controlled data.

• Experience in manufacturing, industrial, or OT-adjacent domains (MES, SCADA, PLC integration, factory automation, IoT).
• Hands-on experience deploying ML models at the edge for computer vision, sensor fusion, or predictive maintenance applications.
• Experience with edge ML frameworks (TensorFlow Lite, ONNX Runtime, OpenVINO, NVIDIA TensorRT) and deploying models on resource-constrained devices.
• Background in computer vision systems, including camera selection, lighting design, image processing pipelines, and ML inference for quality inspection.
• Experience with industrial data protocols (OPC-UA, Modbus, Profinet, EtherNet/IP) and integrating IoT systems with PLCs and industrial control systems.
• Familiarity with frontier AI tooling, AI coding assistants, and AI-enabled software development workflows.
• Experience in hyper-growth startup-like environments, with demonstrated success balancing speed, ambiguity, and long-term system health.
• Familiarity with enterprise systems such as ERP, MES, WMS, or manufacturing planning systems.
• Experience with containerization (Docker) and orchestration (Kubernetes) for edge deployment scenarios.
• Experience in manufacturing industries with hands-on exposure to assembly lines or production environments.
• Background in building ML data collection pipelines, data labeling workflows, or MLOps for manufacturing applications.
• CompTIA IoT Engineer, AWS IoT Core, or Azure IoT certification.
• Experience with time-series databases (InfluxDB, TimescaleDB) and real-time analytics for sensor data.
• Eligible to obtain and maintain a U.S. Secret security clearance.