Robotics & Embedded Systems Engineer

2 нед. назад

ВеликобританияOnsite

embedded softwarecomputer visionhardwareruntime systemssensor integration

Engineer responsible for hardware, low-level software, and onboard runtime for deployed autonomy systems in robotics and embedded systems at .

SPAICE is building the geospatial intelligence layer for autonomous vehicles operating where GNSS is unavailable, unreliable, or denied. Our Spatial AI enables drones and satellites to determine their position, understand their surroundings, and transform every detection into actionable, geo-referenced intelligence. From denied airspace on Earth to complex orbital environments, SPAICE delivers resilient navigation and persistent situational awareness in any condition.
At SPAICE, you’ll work on some of the hardest autonomy challenges across air and space, developing technology for GNSS-independent flight, orbital awareness, formation flying, and autonomous operations. You’ll work alongside a mission-driven team and leading aerospace and defence partners, turning advanced research into systems with a tangible impact on real-world missions.

Multi-sensor platforms flying in contested environments. AI models running in real time on embedded hardware. Fleets of onboard computers that have to work the same way every time, on every aircraft. Behind every SPAICE deployment is a hardware and embedded software foundation built for reliability.
As a Robotics & Embedded Systems Engineer, you'll join the team behind SPAICE's deployed autonomy systems. You'll work directly with our computer vision, navigation and field engineers, and own the hardware, low-level software and onboard runtime that our Spatial AI runs on.
Your work will span hardware integration, embedded Linux, sensor synchronisation, system services, deployment tooling and edge AI optimisation. You'll take our systems onto new vehicles, new computing platforms and larger fleets.
This is a hands-on role for someone who is as comfortable with a soldering iron and an oscilloscope as with a kernel driver or a deployment pipeline.

M.S. in Computer Science, Computer Engineering, Electrical Engineering, Robotics, or a related technical field.
Strong C/C++ and Python skills.
Solid understanding of Linux internals, including boot process, system services, drivers and cross-compilation.
Experience with sensor integration and time synchronisation.
Experience with containers, Linux networking and inter-process communication.
Ability to debug across software, hardware, timing and networking, using tools such as oscilloscopes and logic analysers.
Hands-on hardware skills, including soldering, wiring and reading schematics.
Experience with version control, CI/CD and hardware-in-the-loop testing.
Experience deploying and optimising deep learning models on edge hardware is a plus.
Experience with microcontroller firmware, real-time operating systems or drone autopilots is a plus.
Willingness to travel to test sites.

Competitive compensation.
Equity options – you will join us at the ground floor and share in the upside.
Well‑being perks – access to premium gyms, climbing centers, and wellness programs.
Work on flight-grade autonomy software with industry leaders.
Fast-paced, high-impact environment with genuine ownership.

Hardware integration. Assemble, wire and bring up robotic and aerial platforms, including onboard computers, cameras, inertial and other sensors, radios and flight electronics.
Sensor synchronisation. Design and maintain precise time synchronisation across multiple sensors, and validate it through measurement.
Embedded Linux and firmware. Integrate new sensors and peripherals from drivers to system configuration, and develop low-level software close to the hardware.
Onboard runtime. Develop system services, inter-process communication, logging, health monitoring and automatic recovery for systems that run unattended.
Deployment at scale. Containerise our software, build tooling to provision and update fleets of devices, and develop hardware-in-the-loop test benches integrated into CI/CD.
Edge AI optimisation. Optimise deep learning models for embedded hardware and bring them to new computing platforms and accelerators.
Platform integration and field testing. Integrate with autopilots, flight controllers and ground systems, support flight and field trials, and turn field results into design improvements.