Senior Perception Engineer | Sensors, Firmware & Drivers at Persona AI

Job description

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Job Title: Senior Perception Engineer | Sensors, Firmware & Drivers Department: Software Employment Type: Full-Time Location: Houston, TX Travel: 10% WHO WE ARE Persona AI is building humanoid robots for the most demanding environments in heavy industry — shipyards, steel mills, fabrication facilities, and offshore platforms — performing welding, grinding, maintenance, inspection, and material-handling work that is dangerous, physically demanding, and increasingly difficult to staff. We are backed by leading strategic and financial investors and engaged with global industrial leaders across Korea, Japan, the United States, and Singapore. Korea is the center of gravity for our early commercial strategy, anchored by relationships with the world’s leading shipbuilders and steelmakers. Our work spans both the robot platform itself and the systems, partners, and playbooks required to deploy it at scale. WHY JOIN PERSONA AI? - We offer competitive compensation, a performance-based bonus, 100% employer covered medical benefits for most healthcare plans, early-stage equity, competitive PTO, and company-wide paid winter break between December 24th and January 2nd. - You’ll shape technology that’s redefining the possibilities of robotics and human interaction. - Work alongside passionate teammates who value creativity, and continuous learning. ABOUT THE ROLE As a Senior Perception Engineer focused on Sensors, Firmware & Drivers, you'll own everything between a photon hitting a sensor and the algorithm that acts on it. You'll bring up new imaging and depth sensors from first register read to first light, and write the V4L2 drivers and zero-copy pipelines that move their data on NVIDIA Jetson. You'll also build the time synchronization that keeps cameras, lidar, IMU, and joint state aligned. This is a hands-on role. You'll review sensor boards and harnesses with EE, work through errata with silicon vendors, and debug with a scope in hand. You'll qualify hardware for the real world: welding arcs, heat, vibration, and heavy EMI from industrial equipment. You'll also build the calibration and validation infrastructure that proves it all works. If you enjoy first light on new hardware, care about microseconds, and want to be the team's go-to authority on what the sensor stack can deliver, this role is for you. WHAT YOU WILL BE DOING - Bring up new imaging and depth sensors end to end: I2C/SPI enumeration, register maps, GMSL2/GMSL3 and FPD-Link serializer/deserializer configuration, CSI-2 lane mapping, and first light on target hardware. - Write and maintain V4L2 / MIPI CSI-2 camera drivers and device tree overlays for NVIDIA Jetson platforms (Orin, Thor), including kernel work against the Jetson Linux BSP. - Build low-latency, zero-copy sensor ingest and pre-processing pipelines in NVIDIA Holoscan, GStreamer, and CUDA | ISP tuning, debayer, rectification, format conversion with a hard budget on glass-to-algorithm latency. - Own multi-sensor time synchronization: PTP/gPTP, hardware trigger distribution, exposure-to-timestamp accounting, and drift characterization across cameras, lidar, IMU, and robot joint state. - Partner with EE on schematic and layout review for sensor boards, GMSL coax/harness design, power sequencing, and connector selection; debug with a scope, logic analyzer, and thermal camera in hand. - Characterize and qualify sensors for the real environment: welding-arc dynamic range, IR interference, vibration, ingress, temperature soak, and EMI from high-current equipment. - Build the calibration and validation infrastructure | intrinsics/extrinsics, HDR and exposure behavior, automated bench tests, CI on real hardware. - Work directly with vendors and silicon partners on errata, firmware revisions, and early access to new parts. - Support the perception and autonomy teams as the authority on what the sensor stack can and cannot deliver. WHAT WE ARE LOOKING FOR: - 5+ years building embedded sensor, firmware, or driver systems in robotics, autonomous vehicles, aerospace, or comparable hardware-heavy domains. - Strong C/C++ and Python; comfortable in the Linux kernel. - Bonus: Hands-on experience with NVIDIA Jetson platforms, Jetson Linux BSP, device tree, kernel module development, flashing and board support work. - Demonstrated camera bring-up experience: MIPI CSI-2, V4L2, GMSL2/GMSL3 (Maxim/ADI) or FPD-Link, I2C/SPI sensor configuration. - Experience shipping software that runs on hardware in the field, with the instincts about failure modes that comes from having done it. BONUS SKILLS: - NVIDIA Holoscan SDK, DeepStream, or CUDA/GPU-accelerated streaming pipelines. - Time synchronization across heterogeneous sensors (PTP, hardware trigger design). - ISP tuning, HDR imaging, or low-level camera sensor characterization. - Lidar, radar, ToF, or event-camera integration. - Experience with industrial or safety-relevant hardware environments. Persona AI is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, age, disability, veteran status, or any other characteristic protected by applicable federal, state, or local law.

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Must-haves

Job Title: Senior Perception Engineer | Sensors, Firmware & Drivers
Department: Software
Employment Type: Full-Time
Location: Houston, TX
If you enjoy first light on new hardware, care about microseconds, and want to be the team's go-to authority on what the sensor stack can deliver, this role is for you.
Bring up new imaging and depth sensors end to end: I2C/SPI enumeration, register maps, GMSL2/GMSL3 and FPD-Link serializer/deserializer configuration, CSI-2 lane mapping, and first light on target hardware.
Write and maintain V4L2 / MIPI CSI-2 camera drivers and device tree overlays for NVIDIA Jetson platforms (Orin, Thor), including kernel work against the Jetson Linux BSP.
Build low-latency, zero-copy sensor ingest and pre-processing pipelines in NVIDIA Holoscan, GStreamer, and CUDA | ISP tuning, debayer, rectification, format conversion with a hard budget on glass-to-algorithm latency.
Own multi-sensor time synchronization: PTP/gPTP, hardware trigger distribution, exposure-to-timestamp accounting, and drift characterization across cameras, lidar, IMU, and robot joint state.
Partner with EE on schematic and layout review for sensor boards, GMSL coax/harness design, power sequencing, and connector selection; debug with a scope, logic analyzer, and thermal camera in hand.
Characterize and qualify sensors for the real environment: welding-arc dynamic range, IR interference, vibration, ingress, temperature soak, and EMI from high-current equipment.
Build the calibration and validation infrastructure | intrinsics/extrinsics, HDR and exposure behavior, automated bench tests, CI on real hardware.
Work directly with vendors and silicon partners on errata, firmware revisions, and early access to new parts.
Support the perception and autonomy teams as the authority on what the sensor stack can and cannot deliver.
5+ years building embedded sensor, firmware, or driver systems in robotics, autonomous vehicles, aerospace, or comparable hardware-heavy domains.
Strong C/C++ and Python; comfortable in the Linux kernel.
Demonstrated camera bring-up experience: MIPI CSI-2, V4L2, GMSL2/GMSL3 (Maxim/ADI) or FPD-Link, I2C/SPI sensor configuration.
Experience shipping software that runs on hardware in the field, with the instincts about failure modes that comes from having done it.
NVIDIA Holoscan SDK, DeepStream, or CUDA/GPU-accelerated streaming pipelines.
Time synchronization across heterogeneous sensors (PTP, hardware trigger design).
ISP tuning, HDR imaging, or low-level camera sensor characterization.
Lidar, radar, ToF, or event-camera integration.
Experience with industrial or safety-relevant hardware environments.

Nice-to-haves

Bonus: Hands-on experience with NVIDIA Jetson platforms, Jetson Linux BSP, device tree, kernel module development, flashing and board support work.

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aigopython

Platform: ashby
Effort: quick

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