Imaging Engineer

Raynetics is a deep-tech startup that uses physics and AI to go beyond the limits of imaging hardware. Our software turns raw microscopy data into 3D volumes and chemical maps for nanoscale material inspection. We work mainly in semiconductor metrology and failure analysis, where devices have become small enough that measuring them is nearly as hard as making them. We are now a team of four and growing.

We are looking for an Imaging Engineer to build and refine the algorithms behind our products, from raw instrument signal to final 3D output. The role suits someone who wants to help shape a product and a company from the start, and who wants to do more than one job title suggests. Every day looks different. Algorithm work one week, a grant proposal or a customer conversation the next.

Roles and Responsibilities

• Design, prototype and evaluate algorithms for 3D reconstruction, image processing and analysis, using physics-based modelling, numerical optimization and machine learning.
• Build and maintain production-quality pipelines from raw instrument signal to a usable 3D result.
• Design experiments, benchmark against ground truth, and document the limits of each method.
• Extend our tools to new imaging technologies. Bring your own ideas for markets we could open up.
• Report results through internal presentations, technical documentation, customer meetings and publications.
• Write project proposals and grant applications. Help build our network of academic and industry partners.
• Present our work at conferences, run technical demos, and produce the technical content used in marketing and customer conversations.

Required Qualifications

• MSc or higher in applied physics, applied mathematics, computer science, or a related computational field. A PhD is highly valued.
• Applied experience in imaging or related problems. You have built algorithms, systems or pipelines, not only used them. Industry and academic experience count equally. Experience in the tech or semiconductor industry is a strong plus.
• Solid grounding in linear algebra, numerical methods, optimization, inverse problems and signal processing.
• Working knowledge of machine learning, neural networks and deep learning applied to imaging.
• Proficiency in Python. Experience with C, C++ or similar scientific programming environments is a plus.

Preferred Qualifications

• 3D reconstruction or tomography.
• Physics-informed networks or differentiable forward models.
• Hands-on work with electron microscopy: transmission electron microscopy (TEM) tomography, focused ion beam scanning electron microscopy (FIB-SEM), scanning transmission electron microscopy (STEM), energy dispersive X-ray spectroscopy (EDX). Other 3D and spatial imaging also counts, such as X-ray, computed tomography (CT), cathodoluminescence or light-sheet microscopy.
• Building AI models: architectures, training pipelines, and knowing when a learned method is the wrong tool.
• Advanced materials, semiconductors or the wider tech industry.
• Graphics processing unit (GPU) programming, or making scientific code faster.

We look at what you can do, not the length of your CV. A strong MSc graduate with solid research experience is in scope.

What We Offer

• Compensation based on your profile and experience, plus a share scheme.
• Hard technical problems and a role that grows as fast as the company does.
• A close view of how a deep-tech company gets built: product, funding, intellectual property, market access and commercial strategy.
• A small team where your work is visible.

Practicalities

Location. Anywhere in the Netherlands, preferably within travel distance of Eindhoven or Amsterdam, where our offices are. We work mostly remotely, with occasional team days and events, and some international travel for conferences.

Eligibility. You must have the right to work in the Netherlands. We cannot sponsor work permits.

Hours. Full time, 40 hours per week, flexible schedule.

How to Apply

• Email [email protected] with your CV and a short motivation letter. Tell us about one imaging or algorithm problem you have worked on and what made it hard. Links to code, papers or your thesis are welcome. Standalone applications via linkedin won't be entertained.