Machine Learning & Signal Processing Scientist (Hybrid)

About The Role

We are looking for a skilled and motivated Machine Learning & Signal Processing Scientist to join our team. In this role, you will work on processing and analyzing multi-source environmental and sensor data, including satellites, drones, and in-situ measurements.

Your work will focus on extracting meaningful signals from noisy data, developing robust algorithms, and modeling physical phenomena using advanced statistical and machine learning techniques.

Responsibilities:

Key Responsibilities

• Process and analyze multi-source sensor data, with a strong focus on noise reduction and signal quality improvement
• Develop and apply time series models to characterize and predict signal behavior
• Design and implement machine learning and deep learning models (e.g., CNNs, RNNs) for spatial and temporal signal analysis
• Build algorithms for detection, classification, and quantification of physical phenomena
• Validate models against real-world data and ensure scientific robustness
• Communicate results clearly through visualizations, reports, and presentations to technical and non-technical stakeholders
• Collaborate closely with cross-functional teams to refine and deploy solutions
  
  

• 3+ years of experience in signal processing and machine learning
• Strong expertise in:
• Noise filtering and signal enhancement
• Time series analysis and modeling
• Experience with machine learning and deep learning frameworks
• Familiarity with multi-source sensor data (satellite, drone, or in-situ measurements)
• Experience with remote sensing data, including multispectral and hyperspectral imagery
• Strong programming skills in Python (or equivalent)
• Excellent problem-solving skills and attention to detail
  
  

• Experience with generative AI or advanced representation learning
• Advantage, use of AI/ML Agents (Claude Code) for enhancing productivity
• Background in geospatial analysis or environmental data