{"id":1777959,"url":"https://alion.io/job/galaxeye-space-backend-engineer-ml-systems","title":"Backend Engineer, ML systems","company":{"id":59381,"name":"GalaxEye Space","domain":"galaxeye.space","url":"https://alion.io/company/galaxeye-space","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Zoho Recruit","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":14000,"max_usd":35000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":11},"experience_years_min":1,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Machine Learning","optional":false},{"name":"Docker","optional":true},{"name":"GDAL","optional":true},{"name":"Kubernetes","optional":true},{"name":"ONNX Runtime","optional":true},{"name":"QGIS","optional":true},{"name":"Triton","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-10-03T17:02:56Z","employer_posted_date":"2026-10-03","last_verified_at":"2026-10-07T22:51:20Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"About GalaxEye\nGalaxEye is a Space-Tech startup pioneering the world's first\nOptoSAR Earth Observation Satellite, integrating SAR (Synthetic Aperture\nRadar) and MSI (Multi-Spectral Imaging) on a single platform. As we move\ntowards building a constellation of indigenous satellites, we are also\ndeveloping advanced data platforms that fuse satellite data, AI analytics, and\ngeospatial intelligence.\nAbout the role\nYou'll help build\nthe backend and ML platforms that turn multi-sensor satellite data into\ngeospatial intelligence - and you'll build them to run fully air-gapped and offline, deployed inside defense and\nintelligence environments with no internet access.\nThis is an early-career role. We're not expecting you to have\ndone all of this already. We're hiring for how fast you learn, how you debug\nwhen things are murky, and whether you have the instincts to become genuinely\ngood at a rare intersection: production backend, applied ML, and\nhard-constraint systems that have to work without the cloud crutches most\nengineers lean on.\nWhat makes this different\nMost backend/ML jobs\nlet you reach for a managed service when things get hard - a hosted model API,\ncloud autoscaling, pip install at deploy time. Here\nyou can't. Systems run on isolated, on-prem hardware with no internet at\nruntime. That means:\nModels are self-hosted andrun locally - no external inference endpoints.\n\nDependencies are mirrored andbuilds are reproducible and offline-friendly.\n\nDeployment and updates happenthrough controlled, secure processes, not push-to-cloud.\n\nMonitoring, logging, andevals all have to be self-contained.\n\nIf that sounds like a fun constraint rather than an\nannoyance, you'll fit well here.\nWhat you'll actually do\nBuild and maintain backendservices and APIs that fuse satellite data (SAR + MSI) and serve\ngeospatial analytics to analysts.\n\nDesign and work withdatabases and data flows for large raster/imagery datasets - model the\ndata, write queries and pipelines that hold up at scale.\n\nPut ML models behindreliable, self-hosted services: take something that works in a notebook\nand make it a monitored, production service that runs offline.\n\nWork on inference and MLpipelines for imagery/geospatial analytics - batching, latency vs.\nthroughput, GPU constraints, keeping things healthy in an air-gapped\ndeployment.\n\nContribute to analyst-facingand agentic tooling - components that chain steps, call local tools, and\nsupport intelligence workflows (and fail gracefully when they don't).\n\nDebug systems where\"correct\" is fuzzy, and build the evals and monitoring that tell\nus whether a change actually made things better.\n\nWhat you'll learn here\nBecause this matters\nas much as the work:\nHow to build production MLand data systems under real constraints - offline, on-prem, security-first\n- a skill very few engineers ever develop.\n\nHow to reason about systemsthat are probabilistic-allycorrect, not just pass/fail.\n\nApplied geospatial/EO machinelearning and multi-sensor data fusion, mentored by [a lead with ML\nengineering experience / the team].\n\nHow to stay sharp withoutmanaged services - reading source code and papers, and self-hosting what\nothers just call an API for.\n\nRequirements\nWhat we're looking for\nGenuinely required:\n1-3 years of backend engineering / MLengineering experience.\n\nStrongfundamentals -\nyou understand why, not just which framework method to call. You can reason about\nwhat happens between a request arriving and a response leaving.\n\n Systematicdebugging. When\nsomething breaks, you form a hypothesis, reproduce it, and narrow it down\n- you don't just try random fixes. (This matters double when you can't\ngoogle your way out live.)\n\nEvidence youlearn fast and on your own. You've picked up something hard recently and can\nexplain both the thing and how you learned it.\n\nYou surfaceblockers early and communicate clearly when you're stuck.\n\nComfort withconstraints and process - security discipline, careful data handling, and working within\nan air-gapped environment are part of the job, not obstacles to route\naround.\n\nCuriosityabout ML/AI with\nsome hands-on exposure - ideally you've run a model locally/self-hostedrather than only via a cloud\nAPI.\n\nBonus (nice to have)\nAny of these are a\nplus - we don't expect all or even most:\nGeospatial / remote-sensingexperience: GDAL, rasterio, QGIS, working with satellite imagery, SAR, or\nmulti-spectral data.\n\nSelf-hosting or serving MLmodels on-prem (e.g. Triton, ONNX Runtime, local LLMs, vLLM).\n\nExperience with air-gapped,on-prem, or high-security deployments; reproducible builds; offline\npackage mirroring.\n\nFamiliarity withcontainerization for isolated environments (Docker/K8s), and GPU-based\ninference.\n\nExposure to observability,evals, or testing non-deterministic systems.\n\nComfort reading a paper'smethod section or an unfamiliar library's source.","description_format":"text","description_chars":4845,"description_truncated":false,"requirements":{"experience_years_min":1,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Space & Aerospace","Earth Observation"],"lifecycle":[{"event":"open","at":"2026-10-03T17:02:56Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":3,"expected_fill_days":20,"reasons":["conf:2","velocity","win:early","comp:junior"],"computed_at":"2026-10-07T05:47:15Z"},"pay":null,"html_url":"https://alion.io/job/galaxeye-space-backend-engineer-ml-systems","json_url":"https://alion.io/job/galaxeye-space-backend-engineer-ml-systems.json","meta":{"generated_at":"2026-10-08T01:20:54Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":2436,"day_limit":5000,"remaining_today":2564,"minute_limit":60,"resets_at":"2026-10-09T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":59381},"rest":"https://alion.io/mcp/rest/get_company?id=59381"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fgalaxeye-space-backend-engineer-ml-systems"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fgalaxeye-space-backend-engineer-ml-systems"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fgalaxeye-space-backend-engineer-ml-systems"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/galaxeye-space-backend-engineer-ml-systems\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fgalaxeye-space-backend-engineer-ml-systems"}]}