Backend Engineer, ML systems

About GalaxEye

GalaxEye is a Space-Tech startup pioneering the world's first OptoSAR Earth Observation Satellite, integrating SAR (Synthetic Aperture Radar) and MSI (Multi-Spectral Imaging) on a single platform. As we move towards building a constellation of indigenous satellites, we are also developing advanced data platforms that fuse satellite data, AI analytics, and geospatial intelligence.

About the role

You'll help build the backend and ML platforms that turn multi-sensor satellite data into geospatial intelligence — and you'll build them to run fully air-gapped and offline, deployed inside defense and intelligence environments with no internet access.

This is an early-career role. We're not expecting you to have done all of this already. We're hiring for how fast you learn, how you debug when things are murky, and whether you have the instincts to become genuinely good at a rare intersection: production backend, applied ML, and hard-constraint systems that have to work without the cloud crutches most engineers lean on.

What makes this different

Most backend/ML jobs let you reach for a managed service when things get hard — a hosted model API, cloud autoscaling, pip install at deploy time. Here you can't. Systems run on isolated, on-prem hardware with no internet at runtime. That means:

• Models are self-hosted and run locally — no external inference endpoints.
  
• Dependencies are mirrored and builds are reproducible and offline-friendly.
  
• Deployment and updates happen through controlled, secure processes, not push-to-cloud.
  
• Monitoring, logging, and evals all have to be self-contained.
  

If that sounds like a fun constraint rather than an annoyance, you'll fit well here.

What you'll actually do

• Build and maintain backend services and APIs that fuse satellite data (SAR + MSI) and serve geospatial analytics to analysts.
  
• Design and work with databases and data flows for large raster/imagery datasets — model the data, write queries and pipelines that hold up at scale.
  
• Put ML models behind reliable, self-hosted services: take something that works in a notebook and make it a monitored, production service that runs offline.
  
• Work on inference and ML pipelines for imagery/geospatial analytics — batching, latency vs. throughput, GPU constraints, keeping things healthy in an air-gapped deployment.
  
• Contribute to analyst-facing and agentic tooling — components that chain steps, call local tools, and support intelligence workflows (and fail gracefully when they don't).
  
• Debug systems where "correct" is fuzzy, and build the evals and monitoring that tell us whether a change actually made things better.
   

What you'll learn here

Because this matters as much as the work:

• How to build production ML and data systems under real constraints — offline, on-prem, security-first — a skill very few engineers ever develop.
  
• How to reason about systems that are probabilistic-ally correct, not just pass/fail.
  
• Applied geospatial/EO machine learning and multi-sensor data fusion, mentored by [a lead with ML engineering experience / the team].
  
• How to stay sharp without managed services — reading source code and papers, and self-hosting what others just call an API for.
   

What we're looking for

Genuinely required:

• ~1-2 years of backend engineering / ML engineering experience.
  
• Strong fundamentals — you understand why, not just which framework method to call. You can reason about what happens between a request arriving and a response leaving.
  
• Systematic debugging. When something breaks, you form a hypothesis, reproduce it, and narrow it down — you don't just try random fixes. (This matters double when you can't google your way out live.)
  
• Evidence you learn fast and on your own. You've picked up something hard recently and can explain both the thing and how you learned it.
  
• You surface blockers early and communicate clearly when you're stuck.
  
• Comfort with constraints and process — security discipline, careful data handling, and working within an air-gapped environment are part of the job, not obstacles to route around.
  
• Curiosity about ML/AI with some hands-on exposure — ideally you've run a model locally/self-hosted rather than only via a cloud API.
  

Bonus (nice to have)

Any of these are a plus — we don't expect all or even most:

• Geospatial / remote-sensing experience: GDAL, rasterio, QGIS, working with satellite imagery, SAR, or multi-spectral data.
  
• Self-hosting or serving ML models on-prem (e.g. Triton, ONNX Runtime, local LLMs, vLLM).
  
• Experience with air-gapped, on-prem, or high-security deployments; reproducible builds; offline package mirroring.
  
• Familiarity with containerization for isolated environments (Docker/K8s), and GPU-based inference.
  
• Exposure to observability, evals, or testing non-deterministic systems.
  
• Comfort reading a paper's method section or an unfamiliar library's source.