Agronomy Software Engineer

What this role actually is

We need someone who lives in two worlds. About three-quarters of your time you're a full stack Python focused developer on our software team — building, reviewing, shipping. The other quarter you're sitting with our agronomy team, pulling data from the backend to answer their questions and helping them write better code when they need to write any.

If you came up through plant science or biosystems engineering and taught yourself to write production Python, this might fit. If you came up through CS and spent a few years at an agtech learning what an V3 soybean looks like and why a check plot exists, this might also fit. We care about the combination, not which side you came from.

Day-to-day

• Build and maintain data pipelines that move plot-level phenotypic data from our imagery and trial systems into analysis-ready tables

• Support model development for things like canopy metrics, yield prediction, and product placement

• Pull data from backend tables when an agronomist asks “what data in our system can be leveraged to advance this topic” and they need an answer in a day, not a week

• Review code written by agronomists — usually analysis scripts, not production code — and catch the errors before they end up in a customer report

• Help the agronomy team write better Python: structure their scripts, use the right libraries, version their work

• Translate between teams: when software ships a new data table, you make sure agronomy understands what's in it. When agronomy needs a new field captured, you make sure software knows why and what shape.

What we actually need you to know

Required

• Strong Python: pandas, numpy, geopandas, and the kind of comfort that means you can read someone else's code and tell whether it's wrong

• Experience with full-stack web development is necessary--you will be developing production-ready code for advancing our internal software platform (working knowledge of React JS, .NET, and C# would be ideal)

• SQL good enough to write non-trivial queries against a production database without breaking it

• Experience working with spatial or geospatial data (rasters, shapefiles, coordinate systems — you don't have to be a GIS expert but you can't be confused by what an EPSG code is)

• Working knowledge of statistical analysis as it applies to agricultural field trials: replication, blocking, ANOVA, mixed models. You don't need to be a biometrician; you need to be able to look at an analysis and tell whether it's wrong.

• Familiarity with the agronomy side: you know what a plot is, why check rows matter, what happens when carriers get confounded with treatments, why drone flight timing matters for senescence imaging. If we have to explain growth stages, this isn't the role for you.

Bonus, not required

• Git, code review experience, working in a real software team

• Experience with imagery analysis (multispectral, NDVI, orthomosaics)

• Exposure to YOLO, deep learning, neural nets, segmentation, or other CV approaches applied to crops

• Anything with multispectral platforms (MicaSense, Sentera, etc.) or photogrammetry tools (Metashape, Pix4D, ODM)

• R, if you've used it for trial analysis — useful for working with the agronomy team even if your stack is Python

What you'll actually get out of it

You'll be the bridge between two teams at a small company that does real work on real fields for real customers. You will have outsized impact compared to the same role at a Bayer or Syngenta. The flip side: we're small, things are not always tidy, and you'll be expected to figure things out without a lot of process.

You'll also be working on the kind of agricultural data problems that mostly only get worked on at large companies — multispectral imagery, plot-level extraction, trial analytics for breeders and seed companies — at a place where you can actually see your work hit a customer's hands.

What we want to know about you

In your cover letter (or however you want to send it), tell us:

1. Show us code you've written. A GitHub link is fine. We want to see actual Python you've shipped, not a resume bullet.

2. Walk us through one agronomic analysis you've done — what the question was, what data you had, what method you used, what you concluded. We're more interested in your reasoning than your result.

3. Tell us where you are on the agronomy to software spectrum, honestly. We'd rather know than guess.

Logistics

• Based in Fargo, ND. You will be expected to be in the office.

• Compensation: competitive for the Fargo market and reflective of the hybrid skill set. We'll be specific with serious candidates.

• Benefits, PTO, the usual.

• Reports into the software team with a dotted line to agronomy.

How to Apply

Please submit your resume via LinkedIn and a cover letter (PDF attachment) to ***email_hidden*** with the subject line:

“Agronomy Software Engineer – [Your Name]”

Applications without a cover letter will not be considered.