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-allycorrect, 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.

Requirements

What we're looking for

Genuinely required:

• 1-3 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-hostedrather 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.