Machine Learning Engineer

Job description:

• Design, develop, and optimize machine learning pipelines for space applications (Earth observation, satellite telemetry analysis, spacecraft autonomy, space mission operations).
• Collaborate with our engineers to optimize and deploy ML models to KP Labs’ Data Processing Units.
• Maintain MLOps and CI/CD workflows.
• Stay up to date and be a part of the latest developments in machine learning for space.
• Collaborate with the European Space Agency and top space-related companies and universities in Europe

What we expect:

• MSc or BSc in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.
• Experience building and deploying machine learning solutions in real applications.
• Strong Python programming skills.
• Experience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn.
• Experience with MLOps, CI/CD pipelines, and software engineering best practices.
• Experience with experiment tracking and model management tools (e.g., MLflow, Weights& Biases, DVC, Kubeflow, or similar).
• Practical knowledge of Docker and containerized applications.
• Good communication skills and fluent English (B2 or higher).

Nice to have:

• Experience deploying AI models on edge devices (especially FPGA-based).
• Knowledge of model optimization methods and frameworks
• Experience in the space sector, Earth observation, or robotics.
• Knowledge of Infrastructure as Code tools (Terraform, Ansible, etc.).

What we offer:

• Work with world-class companies and institutions at the cutting edge of the space industry for global market.
• Gain valuable experience with the support of leading specialists in the field.
• Opportunity to attend the most significant global events related to the space industry.
• Flexible working hours and remote work options.
• Co-financing of kindergarten or nursery fees.
• A wide range of benefits to choose from, including sports cards, private healthcare, and more.

We believe in a“human” approach to work and care about work-life balance. Additionally, one of our most significant assets is our attention to mutual support and knowledge exchange at work.