Full-time · On-site (Marseille or Paris) · Founding team · Reports to founders
The sea is the physical layer of the economy—the conduit for our energy, communications, and trade. It is also Europe’s exposed flank. Pelagon is building the answer.
Founded by Aymeric (serial deeptech entrepreneur; founded & exited Scortex, Leading AI solution for manufacturing quality) and Gauthier (Navy officer and McKinsey), Pelagon is building a foundational multimodal acoustic model for autonomous maritime protection (underwater and surface) in service of European sovereignty. It will be deployed on a distributed, autonomous network of vectors (UUVs, buoys, sailbuoys, seabed…) to provide real-time understanding of underwater threats.
France and Europe have world-class acoustic engineers and a long history in underwater warfare. By bridging this expertise with modern physical-AI strategies (as seen in autonomous driving and robotics). Europe is still missing it’s naval NeoPrime, Pelagon aims to become to lead the way in autonomous marine protection—from detection to interception—partnering with leading navies, primes, and neo-primes, while also operating its own fleet as a service.
Pelagon is backed by Hexa, the pioneer startup studio in Europe, which has launched 35+ companies, including unicorns like Front, Aircall, and Spendesk, combining €230m+ ARR and €750m raised.
As Embedded System Lead Engineer, you take end-to-end technical ownership of the hardware, electronics, and embedded stack across a small family of autonomous maritime platforms, some operating on the surface, some beneath it.
What they share matters more than what sets them apart: each must persist at sea for long stretches, sense its surroundings, run our AI on-device, and report back intelligently — all while surviving a brutal environment on a very tight energy budget.
You will lead their technical development, from architecture and component selection through bring-up, integration, sea trials, and iteration. You'll set the engineering bar for the platforms team and shape how we build for years to come.
These platforms exist to carry one thing into the water: our AI. Pelagon trains high-performance detection models in-house on its own stack, and those models have to run on-device, at the edge, on a razor-thin power budget — then push results back to our operations center. Designing the compute-and-power architecture that makes this possible is the heart of the role.
The mandate is simple and uncompromising: get reliable hardware in the water fast, and start collecting data. Every week of persistent at-sea operation compounds. The hardware is the gate to all of it.
Common system architecture across the platforms: power, sensing, compute, and communications.
The edge-AI compute platform — selecting and integrating the processors and accelerators that run our in-house detection models on-device, and extracting maximum inference from every milliwatt.
Electronics design and integration — power distribution, battery systems, motor/thruster control, sensor interfaces (acoustic, pressure, IMU, GNSS, AIS).
The embedded software and autopilot stack, firmware, and the low-level glue that makes autonomy reliable in a brutal, wet, salty environment.
Robustness and efficiency engineering: watertight integrity, power budgets and endurance, thermal behaviour, EMC, and graceful failure modes far from shore.
Hands-on bring-up and sea trials — you'll be on the dock and on the boat, not just at the bench.
Vendor and component decisions, and the build-up of our test and manufacturing practices as we scale from prototype to fielded units.
These are genuinely difficult, and that's the point:
Persistence with a finite power budget. Endurance is everything for a surveillance node — squeezing weeks of useful operation out of a sealed, ship-deployable platform is a relentless optimization problem across power, compute, and duty-cycling.
Reliability with no one to fix it. A system at sea has to fail safe and recover on its own. There is no field technician at 30 nm offshore in a 2 m swell.
Running real AI at the edge, on almost no power. This is the defining challenge of the role. Our in-house models have to run inference on-device, in real time, on platforms with a razor-thin energy budget — then sense, sleep, and wake intelligently to stretch endurance from hours into weeks. Getting high-performance ML to live within milliwatts is what makes the entire system possible.
Sensing in a hostile medium. The ocean fights you at every turn — pressure, corrosion, biofouling, acoustic noise, intermittent comms — and the hardware has to keep sensing and computing through all of it.
Speed without fragility. We are building fast toward sea trials, but a demonstrator that breaks doesn't generate data. You'll have to find the line between moving quickly and building things that survive the ocean.
You are a sharp, senior builder who wants to own a problem, not be handed a ticket. Specifically:
5+ years building real, shipping hardware — embedded systems that had to be efficient, robust, and survive the real world.
Direct experience building drones or autonomous systems. This could come from drone companies (e.g. Parrot), automotive / autonomous vehicles, or the marine autonomy space.
Deep, hands-on command of the full embedded stack: electronics design, power systems, motor/actuator control, sensor integration, microcontrollers and embedded Linux, and firmware close to the metal.
Comfort with autopilot / flight-controller ecosystems (Pixhawk, ArduPilot / ArduSub / PX4 or equivalent) and the realities of real-time control.
Experience deploying AI/ML models at the edge — embedded inference, accelerators (GPU / NPU / TPU-class), model quantization and optimization, and the power-versus-performance tradeoffs that come with it. This is central to what we do.
A bias toward action: you prototype, test, break, and iterate — and you're energized by tight deadlines and a mission that genuinely matters.
Fluency working in a fast, ambiguous, founding-team environment where you define the process as much as follow it.
Bonus points for: mechanical / mechatronic breadth (you can reason about hulls, sealing, and thrust as well as circuits); experience with subsea or marine hardware; comms in degraded / contested environments; CFD or hydrodynamics intuition; or having taken a hardware product from zero to fielded at scale.
Founding ownership. You'll be one of the first engineers and will define the technical foundation of the company. Meaningful equity comes with that.
A mission with weight. This is hardware that protects critical infrastructure and Europe's maritime space.
Real things, in the real ocean, fast. You'll see your work in the water within weeks, not years.
AI-first, at the edge, in the real world. We train our own models and run them where it counts: in the water, on the device. You'll be solving the edge-ML and power problems most companies only talk about.
A sovereign, European deeptech at the inflection point of a market with strong NATO and EU policy momentum behind it.