Engineering Lead - Core, AI & Application

Polysense; Sint-Denijs-Westrem; Sur place; Plein temps

Description du poste

At Polysense, we're completely disrupting the global food industry. We're taking food producers from high-waste, manual production lines to fully automated, optimised ones, and we help them see and think about their process differently, quite literally . Our computer vision systems watch, analyse, and understand food production lines in real time, offloading quality operators from tedious manual tasks. But quality control is only the first stride. With Polysense AutoControl, we optimise process parameters to ensure product quality meets predefined specs every single time.

You'll join us at a pivotal moment. We just closed a $10.7M USD seed round (backed by Felix Capital and Fortino Capital), and we're moving from a handful of flagship deployments (La Lorraine Bakery Group, Lotus, Agristo, and more) to running Qualify and AutoControl across many more plants and customers. Our Engineering team is organised into three squads, Core, AI, and Application, and we're hiring a Lead for each. These are three separate openings; you'll apply to the one that matches your background, and we'll place you on the team where you'll have the most impact.

As Engineering Lead, you'll own a subpart of our engineering team end to end, report directly to Lucas Van Dijck, our CTPO and co-founder, lead and grow a team of 3-5 engineers, and work closely with Product to translate roadmap into shipped, reliable software.

To be clear: this is a product engineering role. You and your team design, build, and ship Qualify and AutoControl themselves. Installing, configuring, and supporting the product on-site at customer plants is owned by our separate Delivery team — you build the product they deploy, not the other way around.

What you'll be doing

• Building a part of the product: you'll design, build, and ship your part of Qualify and AutoControl, a product that is scaling worldwide and creates tangible impact on a daily basis.
• Raising the technical bar: you'll help define the engineering practices, processes, and standards that don't exist yet, as we scale from a handful of flagship deployments to many more plants and customers.
• Team leadership: you'll manage, mentor, and grow a team of 3-5 engineers, setting technical standards and helping them do the best work of their careers.
• Driving execution and delivery: you'll own how your squad actually ships. You'll break the roadmap into concrete tickets, keep the board healthy, track progress against deadlines, unblock your team, and give Product an honest, up-to-date view of what's on track and what's at risk.
• Partnering with Product: you'll work closely with Product to turn what we learn on the factory floor into a roadmap that scales across customers. Field feedback from Delivery and Go-To-Market directly influence what your team builds next.

• 3+ years of hands-on software engineering experience in a relevant domain, plus real experience leading and mentoring engineers.
• Comfortable owning a subpart of a tangible, technical, industrial product end-to-end and partnering closely with Product to shape its roadmap.
• Strong on execution: you keep your team's tickets, deadlines, and roadmap on track without drowning them in process, and you're happy being accountable for what ships and when.
• Structured, first-principles approach to prioritisation, comfortable saying no and explaining why.
• AI-native: you use AI tools daily in your own workflow and in how your team builds software.
• Excited to build a team's practices and standards from close to zero, not just operate inside an existing one.

On top of this shared bar, each team is looking for specific technical depth:

Core Team

• Background in distributed systems, infrastructure, or real-time data pipelines.
• Comfortable owning the reliability of the systems your team builds, which run on real, physical, industrial hardware (cameras, edge devices, plant networks).
• Experience in compliance is a big plus!

AI Team

• Background building and shipping computer vision or machine learning systems in production, not just research.
• Deep understanding of the practical trade-offs in model accuracy, latency, and robustness for real-world, industrial deployments.

Application Team

• Background full-stack, building customer-facing products used daily by non-technical end users.
• Comfortable owning product quality end-to-end, from backend integrations to the UX an operator sees on the plant floor.

Bonus points if you;

• Have a track record of growing a (small) team of engineers
• Have worked with computer vision, IoT, or other hardware-adjacent products.
• Have joined an early-stage startup and helped scale an engineering team from scratch.