Computer Vision Engineer - Soil Microscopy

SoilNext builds the data layer for soil biology - AI microscopy that turns a slide into numbers a farmer can trust, in production today on real farm samples. Two-person team, Dutch patent filed, real commercial traction. The roleYou'll own the computer vision that turns raw microscopy into a trustworthy number, reporting to the CTO. The pipeline already exists and works - trained detectors plus custom morphometry. The near-term work is on both concrete and sequenced roadmap and ample open-ended research. What we're looking forDeep, hands-on detection / instance segmentation - you move the metrics that matter through real error analysis, not just a training loop.Model efficiency and distillation - a genuine feel for FLOPs and what makes CPU inference fast.Rigour about measurement - you anchor claims to a measured ceiling and would rather report an honest number than a flattering one.A track record on heavily imbalanced, long-tailed data - performance on the tail, not just the head.Genuine command of classical computer vision (resolution, image quality, morphometry), used where it beats deep learning.Strong Python and PyTorch; comfortable with Google Cloud, Docker, and CI/CD. Bonus: video instance segmentation and tracking; microscopy or scientific-imaging background; a real interest in soil, ecology, or regenerative agriculture; ML shipped inside an early-stage startup. You're allergic to overclaiming, pragmatic about deep-learning-versus-classical trade-offs, and you think for yourself - you use AI tools like everyone does, but you know where to overrule them. To applyA short note on who you are and why this fits, your CV, and anything that shows your work (GitHub, papers, a model you're proud of). If AI tools are part of how you work, tell us about cases when you overruled them. Email hello@soilnext.com, subject: Computer Vision Engineer.