Embedded Software Engineer, Airships to Cloud at HyLight

About the role

Responsibilities:
- Own and improve two embedded components: the airship payload software (data capture) and the field-truck Ops Upload tool (offload → cloud).
- Ensure reliable capture: guarantee complete, valid LiDAR/imagery/methane data and catch issues pre-flight.
- Build self-healing and resilient behaviour so systems recover without operator intervention.
- Improve field ergonomics: make upload tools simple, fast and traceable for non-developer operators.
- Design and run hardware-in-loop tests and field validations; codify device test suites and release gates.
- Add diagnostics, logging and end-to-end traceability so failures can be root-caused across hardware, network and software.
- Collaborate with operations, data and cloud teams on interfaces, formats and handover guarantees.

Requirements:
- Experience building embedded or firmware software used in real-world operations (C/C++, Rust or equivalent) and interfacing with sensors/storage.
- Familiarity with RTOS or embedded Linux, device drivers, storage protocols and reliable transfer over flaky networks.
- Proven field-testing practice: writing device test harnesses, running on-device CI and validating behaviour on hardware.
- Strong debugging skills across hardware, networks and software, plus version control and release discipline.
- Comfortable working in the field and with operator-focused design and documentation.
- Appetite for autonomy, ownership and using modern developer tools (incl. AI-assisted coding workflows).

Responsibilities

Own and improve the airship payload software and field-truck upload tool to ensure reliable data capture, verified offloading, and resilient uploads across a growing fleet. Work with the data and operations teams to make flight data traceable from capture through cloud handover, diagnose failures across hardware, networks, and software, and prevent recurrence.

Requirements

Candidates should have experience in embedded software, robotics, or hardware-facing engineering, with practical ownership of software used in real-world operations and familiarity with field failure modes. They should be comfortable testing on devices and working in the field, use recent LLM techniques and AI development tools, and demonstrate autonomy, reliability focus, and operator empathy; an engineering degree or equivalent experience is sought.

Key skills

• Embedded Software — in 4 other listings on The Stack
• Robotics — in 18 other listings on The Stack
• Firmware — in 3 other listings on The Stack
• Hardware Integration — in 5 other listings on The Stack
• Reliable Data Capture — not in any other listing on The Stack
• Fault Recovery — not in any other listing on The Stack
• Device Testing — not in any other listing on The Stack
• Fleet Management — not in any other listing on The Stack
• Data Offloading
• Cloud Uploads
• Network Resilience
• Field Diagnostics
• Root Cause Analysis
• LLM Techniques
• AI-Assisted Development
• Operator-Focused Design

On The Stack (as of 2026-10-24)

• HyLight has 1 other listing on The Stack, 1 of them marked as offering visa sponsorship.
• 16 visa-sponsored jobs in France on The Stack, posted in the last 60 days.
• 137 visa-sponsored software engineer jobs in Europe on The Stack, posted in the last 60 days.

Frequently asked questions about HyLight

Is this Embedded Software Engineer, Airships to Cloud role still open?

It was posted on 2026-10-22 and is listed as active on The Stack. Confirm on HyLight's application page before applying.

Does HyLight offer visa sponsorship?

This listing is marked as offering visa sponsorship. Eligibility rules are set by the employer, so check the details on HyLight's site.

Where is this role located?

The listing gives the location as France. It is marked on-site.

What skills does this role ask for?

The listing names: Embedded Software, Robotics, Firmware, Hardware Integration, Reliable Data Capture, Fault Recovery, Device Testing, Fleet Management, Data Offloading, Cloud Uploads, Network Resilience, Field Diagnostics, Root Cause Analysis, LLM Techniques, AI-Assisted Development, Operator-Focused Design.