Most satellite data is noisy before it's useful. Somewhere between raw imagery and a map that actually tells someone whether a farm needs irrigation or a coastline is eroding, there's an engineer doing the work of turning pixels into something actionable. We're hiring a
Remote Sensing Engineer for that work — fully remote, with a salary of $99,511 per year.
What you'd be building
The projects this role touches range widely: climate monitoring, precision agriculture, urban planning, disaster response. Some days you're deep in spectral analysis on a single dataset. Other days you're building out a pipeline that needs to run reliably without anyone babysitting it.
- Analyze satellite and aerial imagery to extract land-use and environmental insights
- Develop algorithms for image classification, spectral analysis, and temporal modeling
- Build and refine machine learning models for geospatial datasets
- Work with data scientists and field experts to validate remote sensing outputs against ground truth
- Turn geospatial findings into visualizations that make sense to people who don't work with raster data all day
- Automate workflows using open-source geospatial tools and cloud platforms
- Contribute to research papers or grant proposals when a project calls for it
What gets you in the door
The minimum education requirement is a
Bachelor's degree in remote sensing, geomatics, environmental science, or a closely related field. Beyond that, we're looking for at least
3 years of hands-on experience with satellite imagery, LiDAR, or UAV-derived data.
- Proficiency in geospatial programming — Python with tools like Rasterio, GDAL, and scikit-learn
- Comfort working with GIS platforms such as QGIS or ArcGIS
- Experience with land cover classification, change detection, or spectral indices
- Ability to explain technical findings to people who aren't technical, without losing the substance in translation
Also a plus
- Experience with cloud platforms like AWS, GCP, or Azure for scalable geospatial workflows
- Background in interdisciplinary or international project work
- Involvement in climate, biodiversity, or sustainable development research
If you don't check every box here, that's fine — apply anyway if the work genuinely interests you. We've hired people who came from academia, adjacent engineering fields, and career breaks, and the fit has worked out.
The stack you'll work in
Google Earth Engine and Sentinel Hub handle large-scale image processing. Python libraries — Rasterio, GDAL, NumPy, scikit-learn — cover most of the modeling and transformation work. Jupyter Notebooks and GitHub keep the code collaborative and versioned. If you've been comparing geospatial roles on Naukri Mitra, you'll notice many postings list a tool stack without explaining how integrated it actually is — here, the pipeline work and the modeling work aren't separate tracks; they're the same job.
How the team actually works
Time zones are spread out, so most coordination happens async — shared docs, recorded updates, the occasional live call when something genuinely needs real-time back-and-forth. Quarterly hackathons give people room to experiment outside their usual project scope, which has occasionally become someone's next project. Mentorship happens informally more often than through a structured program, though there's structure available if you want it.
Pay and benefits
- $99,511 annual salary
- Health and wellness coverage built for remote employees
- Flexible paid time off, including dedicated wellness days
- Parental leave and mental health resources
- Reimbursement for certifications in geospatial AI, data science, or environmental tech
Where this can lead
One engineer on the team moved into a data leadership role after a mentorship arrangement here — no relocation, no restart. That's not the only path. Some people stay deep in technical work for years and get more senior in that lane instead. Both are supported; neither is assumed.
Who tends to do well in this role?
People who are comfortable being wrong in a dataset before being right — remote sensing involves a lot of iteration, and the first model rarely nails it. People who can sit with a stakeholder who doesn't know what a spectral index is and still help them understand what the data shows. Curiosity matters more here than any single tool on the list above.
Applying
Send your resume along with a short note on a geospatial project you're proud of — what the data showed, and what happened once you shared it. That tells us more than a list of Python libraries would.
We welcome applicants from the United States, Canada, the United Kingdom, the European Union, Australia, India, and many other global regions.
