Senior Embedded Systems Engineer

About the role

CYVL is a Physical AI company on a mission to bring Infrastructure Intelligence to every city, town, and community on the planet. We help governments plan, build, and maintain the physical infrastructure that people depend on every day (roads, sidewalks, bridges, utilities, and more) better than ever before. We pioneered an end-to-end approach that combines purpose-built hardware, computer vision, cloud data pipelines, and AI to transform raw LiDAR and imagery into a living, structured understanding of the built world. Our platform turns what used to take months of manual inspection into actionable intelligence that's easy for any agency to use, saving cities time, money, and resources across hundreds of communities nationwide. At CYVL, we're building toward a future where every piece of public infrastructure on Earth is captured, understood, and continuously monitored so the agencies responsible for it and the residents who rely on it can make better decisions, faster. That's the power of Physical AI applied to the real world. We're a fast-moving, high-growth team based in Boston, MA that believes in solving real-world problems with authenticity, simplicity, and boldness. Every member of our team is empowered to take ownership, deliver results, and leave a lasting impact on the communities we serve. About You You're a builder. You've shipped hardware products in scrappy startup environments and you loved doing it. You're equally comfortable reading a schematic, writing a Linux kernel driver, and debugging a sensor system on the roof of a vehicle in the field. We're looking for a Senior Embedded Systems Engineer to own the design, build, and productionization of the Cyvl Sensor, our next-generation mobile data collection platform integrating LiDAR, 360-degree cameras, GPS, and IMU on an Nvidia SoM. Your job is to harden it, scale it, and ultimately bring key subsystems in-house as we grow from a handful of units to fleet-scale deployment across tens of thousands of vehicles.

Responsibilities

• Own the full hardware platform lifecycle for the Cyvl Sensor: architecture, component selection, PCB/carrier board design, prototyping, testing, and production readiness.
• Design and integrate multi-sensor systems (LiDAR, cameras, GPS, IMU) with Nvidia Jetson Orin or Thor SoMs, including power delivery, signal integrity, and time synchronization (PTP/PPS).
• Develop and maintain the embedded Linux BSP: device trees, kernel modules, camera/sensor drivers, and system bring-up on Nvidia JetPack.
• Drive the transition from off-the-shelf components to custom, in-house designs as the platform scales from prototype to fleet volume.
• Collaborate with the ML team to ensure the hardware platform supports edge AI inference workloads (model deployment, TensorRT optimization is a plus, not a requirement).
• Support mechanical and thermal design for ruggedized, fanless, vehicle-mounted enclosures (IP65, wide temperature range, vibration/shock tolerant).

Requirements

• Required
• 5+ years of experience in embedded systems engineering, hardware design, or a closely related role.
• Hands-on PCB design experience (schematic capture, layout, bring-up, and debug) with tools such as Altium, KiCad, or Eagle.
• Deep embedded Linux proficiency: BSP development, device trees, kernel module development, driver authoring (camera, sensor, USB, Ethernet).
• Experience integrating Nvidia Jetson SoMs (Orin, Xavier, or similar).
• Experience with GMSL2 or MIPI CSI camera interfaces and high-bandwidth sensor integration.
• Familiarity with multi-sensor time synchronization (IEEE 1588 PTP, PPS, NTP).
• Experience with ruggedization: thermal management, vibration/shock mitigation, environmental sealing (IP65+), power architecture for vehicle-mounted systems.
• Track record of shipping hardware products from prototype or low-volume stage through to reliable, scalable production.
• Comfortable working in a startup environment with high autonomy, ambiguity, and ownership.
• BS or MS in Electrical Engineering, Computer Engineering, or equivalent hands-on experience.
• Experience with edge AI inference optimization (TensorRT, DeepStream, CUDA) on Nvidia platforms.
• Mechanical/enclosure design skills (CAD, thermal simulation, DFM for sheet metal or injection molding).
• Background in autonomous vehicles, robotics, mobile mapping, or fleet-deployed sensor systems.
• Experience with LiDAR integration (Ouster, Velodyne, Hesai, or similar) including point cloud data pipelines.
• Familiarity with cellular modem integration, GPS/GNSS receivers, and vehicle power systems (12V/24V automotive).
• Experience scaling hardware from single-digit prototypes to thousands of units (supply chain, vendor management, test fixtures).

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