You will join ICEYE's Intelligence, Surveillance and Reconnaissance (ISR) AI team, building models that turn satellite imagery into intelligence for defense customers. Your role involves working with data scientists to build sustainable infrastructure, workflows, and deployment practices for AI models, both in the cloud and for on-premise systems. This is a hands-on engineering role focused on deploying and running ML models in production.
🏢 About ICEYE
ICEYE is the world leader in sovereign intelligence from space, delivering persistent monitoring capabilities to detect and respond to changes in any location on Earth. We own the world's largest and most advanced SAR (synthetic aperture radar) satellite constellation, providing intelligence with unmatched quality, latency, and revisit times, in any weather, day or night. Founded and headquartered in Finland, ICEYE operates globally with over 1000 employees across Europe, North America, the Middle East, and Asia-Pacific.
🎯 The Role
You will sit with the AI team and work with them to build the infrastructure they train, evaluate, and ship on. Like our platform work, every environment is temporary and must be reproducible from code; a model is only done when it runs, monitored, on hardware we don't control.
✅ Key Responsibilities
Build training, evaluation, and packaging pipelines that take a model from experiment to versioned, deployable release.
Deploy and serve models on Kubernetes with GPUs, in the cloud and on-prem, including air-gapped sites.
Package models, weights, and dependencies so they install and upgrade offline.
Manage GPU compute: scheduling, drivers, utilization, and cost, across cloud and on-prem hardware.
Track data, experiments, and model lineage, so any result can be reproduced and audited.
Monitor models in production for performance, drift, and failures, without relying on outside connectivity.
Write tooling in Python that the AI team uses every day, and work in the model codebase alongside them.
📌 Required Qualifications
Senior hands-on engineer (not an engineering manager or architect role).
Proven experience deploying ML models to production and running them there, not only training them.
Strong Python, and comfort reading and changing ML code (PyTorch or similar).
Hands-on Kubernetes and containers, including GPU workloads.
Infrastructure as code and CI/CD for ML (for example Terraform, Helm, GitHub Actions).
Daily use of AI tools in engineering work, beyond chat: generating and reviewing code, configuration, and tests.
High autonomy: you find problems, propose fixes, and drive them through.
Motivation to work in new defense: building technology used by defense forces.
⭐ Desirable Experience
Deployed models to on-prem, edge, or air-gapped environments.
Model serving and optimization: Triton, TorchServe, KServe, ONNX, TensorRT, quantization.
Computer vision or geospatial data (satellite, SAR, raster, PostGIS).
ML pipeline and tracking tools such as MLflow, Kubeflow, Argo Workflows, DVC, or Weights & Biases.
Large-scale data processing for imagery (Dask, Ray, Spark, object storage).
Strong IC track record in a startup or a fast-changing company launching new products.
Clear communicator who works well with researchers and experienced engineers.
🎁 Benefits
Our benefits are designed to support your health and wellbeing, at work and beyond. We keep improving them based on employee feedback, and offerings vary by location. Talent Acquisition will confirm what applies for this role and location during the process.
🛂 Visa & Eligibility
Employment is subject to applicable security screening (incl. SUPO, where required).
