Senior ML Engineer, Data Ops (Remote Sensing)

Carbon Mapper, Inc.; United States; Hybrid; USD 160,000 - 185,000; Full time

Benefits offered by this job

Health coverage

Generous time-off policy

Professional development

Job summary

Carbon Mapper, Inc. seeks a Senior Machine Learning Engineer to drive ML for remote sensing data products, improving quality, latency, and cost as operations scale. You will deploy models for plume detection, imagery analysis, and infrastructure mapping, while collaborating across Data Operations, Science, and Engineering teams.

You will mentor peers, design scalable pipelines, and emphasize model explainability and reproducibility in production.

Qualifications

• Advanced degree or equivalent experience in Earth Science, Atmospheric Science, Remote Sensing, CS, Stats, AI or related field.
• Experience building ML models with remote sensing data, including deep learning approaches and frameworks like PyTorch or TensorFlow.
• Experience deploying and monitoring ML models in production with observability and reproducibility.

Responsibilities

• Design, develop, and deploy deep learning models for remote sensing imagery analysis, classification, and segmentation.
• Integrate diverse geospatial datasets to improve models and performance.
• Lead data-quality initiatives end-to-end and drive improvements with ML and automation.
• Mentor teammates and communicate ML concepts to non-technical stakeholders.
• Build scalable training/inference pipelines balancing performance, latency, and cost.

Skills

Deep learning

Remote sensing

Python

ML lifecycle

Model explainability

Communication

Education

Advanced degree or equivalent in Earth Science/Remote Sensing/CS/Stats/AI

Tools

PyTorch

TensorFlow

Cloud platforms (GCP/AWS/Azure)

Job description

Carbon Mapper, Inc. seeks a Senior Machine Learning Engineer to drive ML for remote sensing data products, improving quality, latency, and cost as operations scale. You will deploy models for plume detection, imagery analysis, and infrastructure mapping, while collaborating across Data Operations, Science, and Engineering teams.

You will mentor peers, design scalable pipelines, and emphasize model explainability and reproducibility in production.