Backend Engineer, ML systems

About GalaxEye

GalaxEye is a Space-Tech startup pioneering the world's first
Multi-Sensor 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.
   

Requirements

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.