Role: Machine Learning Scientist

Location: Singapore

We are partnering with a leading climate-tech company that is seeking a Machine Learning Scientist to join their core team and advance the frontier of climate impact. In this role, the successful candidate will collaborate closely with the founding team to develop and scale AI models that extract actionable insights from satellite and remote sensing data. The work will directly support applications in climate risk and environmental monitoring. This opportunity is ideal for professionals who thrive at the intersection of applied machine learning, geospatial data, and real-world impact.

Responsibilities

• Develop, train, and deploy ML models using satellite imagery (optical, SAR, multi-modal)
• Integrate physical modelling concepts into AI architectures for better generalization and reduced data dependency
• Collaborate with the engineering team to deliver scalable pipelines for model training and inference
• Contribute to research, publications, and open-source projects where relevant
• Translate technical findings into clear insights for internal teams and external stakeholders

Requirements

• PhD in Machine Learning, Computer Vision, Remote Sensing, or a related field – OR MSc with 4+ years of research or industry experience in ML or equivalent experience.
• 3+ years hands-on experience with PyTorch or TensorFlow
• Proficient in Python/C++; solid grasp of CNNs, attention mechanisms, and optimization tricks
• Demonstrated track record of shipping or publishing ML models using imagery data (e.g., CVPR, NeurIPS, AAAI, or high-quality open-source repositories)
• Working knowledge of geospatial tools (GDAL, Rasterio, QGIS) and cloud compute infrastructure (AWS/GCP, GPUs)
• Comfortable with modern DevOps tools: Git, Docker, CI/CD
• Excellent written and spoken English; ability to distill complex ideas for non-expert audiences

Preferred (Nice-to-Have)

• Exposure to SAR data, physics-guided networks, or multi-modal fusion
• Experience generating or curating synthetic datasets
• Familiarity with ONNX/TensorRT, mixed-precision training, or vector-DB-backed retrieval
• Previous collaboration with EU/US research labs or startups
• Passion for climate-tech, agro-insurance, or environmental compliance