- Strong applied capability in machine learning, data science or applied AI ,
- Strong Python capability and fluency with tools such as TensorFlow, PyTorch or Scikit-learn ,
- Understanding of time-series modelling and anomaly detection ,
- Background deploying ML systems into production environments ,
- Familiarity with spacecraft telemetry or aerospace systems is a plus ,
- An analytical mindset and structured problem-solving approach ,
- To apply, you must have the legal right to work in your chosen location
- The CTO division is where Open Cosmos technology comes to life. Covering engineering, product development, and technical innovation, the team designs, builds, and operates the satellites, systems, and platforms that make our missions possible ,
- It’s a highly collaborative environment where ideas become real hardware, software, and data solutions that deliver impact from space ,
- We’re looking for a Machine Learning Engineer to help build the intelligent automation layer behind Open Cosmos’ growing spacecraft fleet ,
- Working within the Mission Operations Team you’ll design and deliver ML-driven solutions that power anomaly detection, forecasting, operational insights and automated decision-making ,
- This is a hands-on role focused on building models, pipelines and inference systems that plug directly into mission control and production automation tools ,
- Developing and refining models for anomaly detection, telemetry classification, forecasting and behavioural prediction ,
- Building automated data analysis pipelines for mission telemetry and production test data ,
- Integrating ML outputs into operational tools and automation frameworks across mission control and testing ,
- Preparing, cleaning and structuring datasets for training and validation ,
- Analysing trends, surfacing insights and supporting model validation and performance tracking ,
- Maintaining high standards of data quality, reliability and traceability ,
- Working with OpenOPS to embed ML-driven insights into the Mission Control System ,
- Partnering with Automation Engineers to embed ML into operational and production workflows ,
- Validating model behaviour in simulated and live operational contexts ,
- Monitoring model performance and improving production systems over time ,
- Maintaining clear documentation for models, datasets and operational interfaces