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ICEYE

SAR satellite constellation for Earth monitoring

Senior MLOps Engineer - ISR

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeBerlin, GermanyNo salary listed

About the Job

Requirements

  • The candidate must be a senior hands-on engineer; this is not an engineering manager or architect role.
  • The candidate must have proven experience deploying machine learning models to production and operating them there, not only training them.
  • The candidate must have strong Python skills and be comfortable reading and changing machine learning code, such as PyTorch.
  • The candidate must have hands-on experience with Kubernetes and containers, including GPU workloads.
  • The candidate must have experience with infrastructure as code and continuous integration and continuous delivery for machine learning, such as Terraform, Helm, or GitHub Actions.
  • The candidate must use AI tools daily in engineering work beyond chat, including generating and reviewing code, configuration, and tests.
  • The candidate must be able to work autonomously, identify problems, propose fixes, and drive them through.
  • The candidate must be motivated to build technology used by defense forces.

Responsibilities

  • Build training, evaluation, and packaging pipelines that take a model from experiment to a versioned, deployable release.
  • Deploy and serve models on Kubernetes with GPUs, in the cloud and on-premises, including at air-gapped sites.
  • Package models, weights, and dependencies so they can be installed and upgraded offline.
  • Manage GPU compute scheduling, drivers, utilization, and cost across cloud and on-premises hardware.
  • Track data, experiments, and model lineage so results can be reproduced and audited.
  • Monitor production models for performance, drift, and failures without relying on external connectivity.
  • Write tools in Python for daily use by the AI team and work alongside the team in the model codebase.

Desired Qualifications

  • Experience deploying models to on-premises, edge, or air-gapped environments.
  • Experience with model serving and optimization tools such as Triton, TorchServe, KServe, ONNX, and TensorRT, and with quantization.
  • Experience with computer vision or geospatial data, including satellite imagery, synthetic aperture radar, raster data, or PostGIS.
  • Experience with machine learning pipeline and tracking tools such as MLflow, Kubeflow, Argo Workflows, DVC, or Weights & Biases.
  • Experience with large-scale imagery data processing using Dask, Ray, Spark, or object storage.
  • A strong individual-contributor track record in a startup or fast-changing company launching new products.
  • Ability to communicate clearly and work well with researchers and experienced engineers.

About the Company

ICEYE

SAR satellite constellation for Earth monitoring

ICEYE builds and operates the world’s largest synthetic aperture radar (SAR) satellite constellation to monitor the Earth continuously, day or night and in any weather. Its SAR satellites capture radar imagery that can see through clouds and darkness, giving customers in defence and intelligence, insurance, emergency management, and environmental monitoring a persistent view of wide areas. Images and analytics are delivered from a network of satellites and, increasingly, sovereign constellations that governments can operate themselves. ICEYE plans to scale up production toward about 100 satellites per year by 2028 and has pursued large funding rounds to expand capacity and sovereign delivery, aiming to grow its revenue and backlog while offering reliable, near-real-time Earth observation data.

Company size
1,001-5,000
Company stage
Series F
Total funding
$1.5B
Headquarters
Espoo, Finland
Founded
2012

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