Climate ML Engineer: Satellite Data for AgTech

CarbonFarm Technology; Paris; On-site; EUR 70,000 - 110,000; Full time

Job summary

CarbonFarm Technology seeks a climate-passionate Machine Learning Engineer to turn terabytes of satellite data into actionable farming insights. You’ll work with satellite imagery and IoT data to build a deep understanding of agricultural practices worldwide, collaborating with a global team.

Based in Paris or willing to relocate, you’ll clearly communicate complex models, contribute to production deployments, and advance remote sensing research with rigorous validation and bias mitigation.

Qualifications

• Master’s degree or equivalent in data science, computer science, or remote sensing.
• 3+ years of experience with PyTorch or TensorFlow.
• Proficient with SQL/OLAP databases.
• Strong foundations in statistics and probability.
• Fluent in English; Paris-based or willing to relocate.
• Organised, independent and solution-oriented.

Responsibilities

• Collaborate with ML/AI/Data engineers to develop models and algorithms from satellite and IoT data.
• Propose and implement pre-training and fine-tuning approaches for deep models.
• Deploy models in production with the chief architect.
• Research remote sensing approaches from the scientific community.
• Test and validate models with temporal/geospatial cross-validation and bias leakage mitigation.
• Leverage AI tooling for rapid iteration with technical accuracy.

Skills

PyTorch

TensorFlow

Statistics

English fluency

Education

Master’s degree in data science / CS / remote sensing

Tools

SQL

OLAP databases

Job description

CarbonFarm Technology seeks a climate-passionate Machine Learning Engineer to turn terabytes of satellite data into actionable farming insights. You’ll work with satellite imagery and IoT data to build a deep understanding of agricultural practices worldwide, collaborating with a global team.

Based in Paris or willing to relocate, you’ll clearly communicate complex models, contribute to production deployments, and advance remote sensing research with rigorous validation and bias mitigation.