Sensors Wearable Jobs

and robotics customers validate their perception systems against high-fidelity, physically accurate simulated sensor data.

large-scale datasets from autonomous vehicle sensors Sensor Validation and characterization, including analyzing performance metrics for sensing

Experience with Ethernet-based sensor streaming and sensor bridges.

In this role you will work with our research team to apply state of the art approaches to model worlds and sensors such as Lidars, Radars and Cameras.

prototype, build, integrate, debug, and test hardware on real aircraft, not just review designs Define and drive survivability: determine how sensors

run themselves Debug across the cloud/vehicle boundary — tracing root causes through orchestration and application logs one day, verifying how a sensor

Design and run environmental and durability testing, analyze failures, and drive root cause and design fixes Lead integration of sensors

Develop multi-sensor fusion frameworks that integrate cameras, LiDAR, depth sensors, and IMUs for robust real-time state estimation and mapping in dynamic

At Applied Intuition, you will: Define the strategy and roadmap for hardware products, from sensors and compute platforms to vehicle integration

variation due to temperature or other on-road and off-road conditions Enable sensor fusion and work with a variety of sensors including Camera, Lidar

Model inference pipelines Design and optimize storage solutions for structured, unstructured, and multimodal data (e.g., sensor

At Applied Intuition, you will: Design and review schematics and PCB layouts for compute, sensor, and interface hardware

Experience with sensor fusion, offline trajectory reconstruction, coverage analysis, or active data collection.

Nice to have: Experience with ruggedized industrial compute, electronic control units (ECUs), sensor integration, or complex wire

You can discuss fidelity, sensor models, determinism, distributed sim at scale, and HIL with engineers, and you know when a sim result is good enough to trust.