Neural Grader is building AI for automated wood defect detection and grading in real industrial sawmill environments.

We are looking foran interdisciplinaryprofile: someone with a strongforestry / wood processing /lumbergradingbackground ,and digital competenceswho can own the quality loop of our AI defect models.

Ideally, the person should have graduated from TH Rosenheim, Universität Hamburg (Wood Science), TU Dresden, or a similar wood engineering / wood science / forestry school.

This is not a classic support role and not a general coordination role.

The core mission is to own and improve the fulldata → model → customer feedback loop for defect detection:

investigate real customer failure cases
understand whether the issue comes from data, annotation, model limitations, or production conditions
improve evaluation datasets and validation logic
work closely with annotation and ML teams
help turn raw customer feedback into clear, actionable priorities
shorten the loop from “model got it wrong” to “fix validated in production”

You should be comfortable discussing wood defects with customers, reviewing real production cases, and working with technical teams to improve model quality over time.

Ideal background:

Forestry, Wood Engineering, Wood Science, or Wood Processing
strong practical understanding of wood defects and sawmill reality
attention to detail and structured thinking
ability to work with data, examples, quality issues, and technical workflows
good communicationskills in customer and internal discussions

This role sits at the intersection ofwoodexpertise, AI model quality, annotation quality, and customer reality .

Ready to Apply? We're excited to hear from you! Send us your application and let's start the conversation about your future with us.

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