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Berlin · Berlin, Berlin/Brandenburg metropolitan region, Germany
Actively hiring, first seen by us todayReports to: VP of ISR Analytics Employment type: Permanent Workplace model: Hybrid
Employment type: Full-timeFrom the employer’s posting
Department: Engineering › ISR Analytics › ISR EngineeringFrom the employer’s posting
From the employer’s posting.
To our customers we provide intelligence with unmatched quality, latency and revisit times, in any weather, day or night.
In the employer’s words · Read in context · Company website ↗
Actively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
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Day 0
Early. Similar jobs usually come down between day 7 and day 72.
From 4,777 similar postings that came down. Coming down is not always a hire.
3 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordAdd your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- 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. What we’re looking for
- 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.
- 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.
- 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).
- 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).
- 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.
- 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.
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New ML Engineer postings at the employers we follow are up 13% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Senior MLOps Engineer, ISR
Location: Berlin, Germany
Department: ISR Analytics
Reports to: VP of ISR Analytics
Employment type: Permanent
Workplace model: Hybrid
Employment is subject to applicable security screening (incl. SUPO, where required)
ICEYE's Intelligence, Surveillance and Reconnaissance (ISR) AI team builds models that turn satellite imagery into intelligence for defense customers. Your job is to work with data scientists to build sustainable infrastructure, workflows, and deployment practices for the AI models, both in the cloud and for on-premise systems. You 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.
ICEYE is the world leader in sovereign intelligence from space. We deliver persistent monitoring capabilities to detect and respond to changes in any location on Earth.
ICEYE owns the world's largest and most advanced SAR (synthetic aperture radar) satellite constellation. To our customers we provide intelligence with unmatched quality, latency and revisit times, in any weather, day or night. To governments who choose to operate their own constellation we provide this proven capability as a sovereign system.
ICEYE-built constellations serve customers in defence and intelligence, environmental monitoring, insurance and emergency management. We enable fast decisions that contribute to a safer future.
Founded and headquartered in Finland, ICEYE operates globally with over 1000 employees across Europe, North America, the Middle East, and Asia-Pacific.
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.
Must haves:
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.
Nice to haves:
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.
At ICEYE, you’ll join a diverse and highly engaged team united by the ambition to make the impossible possible. As a global scale-up, we combine speed and ambition with the opportunity to take real ownership from day one. Your growth, wellbeing, and success are a priority, with continuous professional development, training opportunities, and a culture where collaboration is how we win.
Make the impossible possible: We set ambitious goals and stay calm under pressure. We bring grit, optimism, and ownership when things get hard, and we keep moving until we find a way.
Be curious: Go deep, ask questions, listen carefully, and think critically. Understand the “why” behind decisions.
See the big picture: Stay close to what’s happening across the company so you can make better decisions. Consider how your work affects others.
Drive effective teamwork: Create psychological safety, invite different perspectives, and build inclusive teams. There are no bad questions.
Act as one team: We win together. We match tasks to the right owner and stay agile as priorities shift.
Have fun: What we do matters—and it should be enjoyable. Celebrate progress, take pride in results, and share the wins.
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.
We want ICEYE to be a place where people can be themselves and do great work. Different backgrounds and perspectives make us stronger, which is why we work to create an environment where people feel included, respected, and able to speak up. Whatever your background, we want you to bring your authentic self to the table.
We’re committed to fair, inclusive hiring and equal opportunity. Everyone is welcome to apply. If you need any adjustments or support during the recruitment process, tell us—we’ll do our best to help.
Apply now to start your ICEYE journey, and help us continue to make the impossible possible together. Read more about ICEYE and working with us at iceye.com.
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Berlin, Berlin/Brandenburg metropolitan region, Germany
- Employment type: Permanent - Workplace model: Hybrid - Employment is subject to applicable security screening (incl. SUPO, where required)
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