Control Systems Engineer

, we're on a mission to power a sustainable future by leveraging cutting‑edge AI in greenhouse technology to deliver more fresh produce to the world. Our values drive everything we do – we work closely with growers, embrace diversity, learn continuously, deliver impactful results, and plant solutions for a resilient food supply.

What we would like you to get excited about:

Architect and operate robust control pipelines  that run 24/7, autonomously managing irrigation worldwide – systems that must be reliable, observable, and safe to fail gracefully.

Design and implement data‑driven control logic , such as model predictive control and adaptive feedback mechanisms that respond in real time to plant state, sensor noise, and environmental dynamics.

Own the full system lifecycle  – from initial design and experimentation through deployment, monitoring, and continuous improvement in live production.

Bridge control theory and data science  to build solutions grounded in physical system dynamics.

Work directly with Plant Scientists and expert growers  to translate biological knowledge into control logic and feedback constraints.

Push the boundary  where control engineering, signal processing, and applied machine learning converge to deliver state‑of‑the‑art products for growers globally.

You have:

An MSc or PhD  in Control Engineering, Robotics, Mechatronics, Applied Mathematics, Systems Engineering, or a related discipline with a strong control systems component.

At least  4 years of experience  in a relevant discipline.

Demonstrated experience building and operating feedback control systems  – you have designed, deployed, or maintained a live control loop.

Solid signal processing skills  – filtering, noise handling, frequency‑domain analysis, or sensor fusion; you know what to do when your data is messy and your sensors lie.

Familiarity with data‑driven control methods  such as model predictive control, system identification, or state estimation (Kalman filtering or equivalent).

A systems mindset  – you think about what happens when things go wrong (sensor failures, edge cases, degraded modes) and design for resilience as well as performance.

Strong proficiency in Python  for production‑grade systems.

Experience with data‑driven modelling for physical systems  – regression, time series, or physics‑informed approaches applied to real process data.

Proficiency with AI tools  – we use them to scale our output; AI is your collaborator, but you are accountable for the result.

Clear, concise communication skills  – the ability to explain control system behaviour to non‑technical stakeholders, including growers and product teams.

Nice to have:

Experience with ML model development and deployment.

Background in process control, agricultural systems, or biological/physical modelling.

Familiarity with time‑series databases, IoT sensor pipelines, or embedded system constraints.

Our offer in return for your talent and skills:

A hybrid work environment – office days on Mondays and Thursdays.

Lunch at the office.

Flexible hours, always respecting your team and meetings.

Pension contribution of 4.5%.

Mental well‑being guidance through Open Up.

Mac Book Pro 16".

Travel allowance for office commute.

Annual learning budget of €1,000.

One‑off work‑from‑home budget of €550.

Monthly Wi‑Fi and phone plan reimbursement of €50.

Unlimited holidays, with an expectation that you take at least 25 per year.

Ready to grow with us?
At Source, we highly value diversity of backgrounds and perspectives. Research shows that people assess their fit for roles differently, so we strongly encourage you to apply even if you feel you don’t tick all the boxes.

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