Machine Learning Engineer-Remote Sensing SME | Hybrid in Berlin| 3-4 stage process | €70-90k per annum
I am partnered with a client based in Berlin looking to expand their ML team and I am searching for a talented Machine Learning Engineer with expertise in remote sensing to join their team in Berlin (flexible Hybrid work arrangements possible). In this role, you will play a key part in developing and deploying machine learning models that extract valuable information from satellite imagery and other remote sensing data sources.
Responsibilities:
- Design, develop, and implement machine learning models for tasks such as object detection, classification, and time series analysis of remote sensing data.
- Pre-process and prepare remote sensing data for machine learning applications.
- Develop and manage data pipelines for efficient data ingestion and processing.
- Collaborate with data scientists and engineers to define project requirements and ensure successful model integration.
- Stay up-to-date on the latest advancements in remote sensing and machine learning.
- Communicate technical concepts effectively to both technical and non-technical audiences.
Qualifications:
- Master's degree in Computer Science, Engineering, Physics, or a related field.
- Minimum 3 years of experience in machine learning with a focus on remote sensing applications.
- Proven experience in developing and deploying machine learning models in production environments.
- Strong programming skills in Python (familiarity with libraries like scikit-learn, TensorFlow, or PyTorch is a plus).
- Experience with data pre-processing techniques for remote sensing data (e.g., radiometric correction, geometric correction).
- Excellent communication and collaboration skills.
Benefits:
- Competitive salary and benefits package.
- Opportunity to work on challenging and impactful projects.
- Collaborative and supportive work environment.
- Flexible work arrangements (remote work possible).
- Continuous learning and development opportunities.
Please apply with your CV, email and contact number. Or alternatively reach out to me on LinkedIn