Machine Learning Scientist

Cygnify; Singapore; On-site; SGD 100,000 - 150,000; Full time

Job summary

A leading climate-tech company in Singapore is seeking a Machine Learning Scientist to develop and deploy AI models. This role involves working with satellite imagery to support climate risk and environmental monitoring. The ideal candidate has a PhD or MSc with substantial experience in machine learning, particularly with PyTorch or TensorFlow, and a passion for climate technology.

Qualifications

• PhD in Machine Learning, Computer Vision, Remote Sensing, or related field.
• 3+ years hands-on experience with PyTorch or TensorFlow.
• Proficient in Python/C++; solid grasp of CNNs and optimization techniques.
• Demonstrated track record of publishing ML models using imagery data.
• Working knowledge of geospatial tools and cloud infrastructure.
• Comfortable with modern DevOps tools.

Responsibilities

• Develop, train, and deploy ML models using satellite imagery.
• Integrate physical models into AI architectures for better performance.
• Work with engineering to deliver scalable pipelines.
• Contribute to research and publications.
• Translate technical findings into actionable insights.

Skills

Machine Learning

Computer Vision

Remote Sensing

Python

C++

PyTorch

TensorFlow

DevOps tools

Geospatial tools

Excellent English

Education

PhD in Machine Learning or related field

MSc with 4+ years experience

Tools

Git

Docker

CI/CD

GDAL

Rasterio

QGIS

Job description

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