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Women in Cleantech and Sustainability
Women in Cleantech and Sustainability

SENIOR MACHINE LEARNING ENGINEER AT CARBON MAPPER (REMOTE)

Location

Remote restrictions apply
See all remote locations

Salary Estimate

N/AIconOpenNewWindows

Seniority

Senior

Tech stacks

Machine Learning
Data
Automation
+18

Permanent role
8 days ago
Apply now

Senior Machine Learning Engineer | Carbon Mapper | Remote

As a Senior Machine Learning Engineer on our Data Operations team, you’ll help ensure our core data products meet a reliable baseline of quality, latency, and cost as we scale. You’ll use operational metrics to pinpoint bottlenecks and quality gaps, then partner with peer teams to fix them through machine learning, automation, and process improvement. You’ll bring deep ML expertise to a domain-heavy problem space (remote sensing, plume detection, and infrastructure mapping) and connect that expertise directly to the operational outcomes our data products depend on.

Essential Duties And Responsibilities

  • Design, develop, and deploy deep learning and machine learning models for remote sensing imagery analysis, classification, and segmentation. Applications include plume detection, hyperspectral data processing, and infrastructure mapping.
  • Identify and integrate remote sensing and other spatial datasets (RGB imagery, basemaps, weather data, GIS inventories) to develop and improve models, using AI-assisted tooling to accelerate data exploration and profiling.
  • Bring current best practices in remote sensing and machine learning to cross-functional discussions on product design and implementation.
  • Mentor and provide technical guidance to less experienced team members, fostering skill development in ML, data engineering, and remote sensing across the Data Operations team.
  • Lead significant data-quality initiatives end-to-end, from problem definition through deployment, collaborating with members of the Data Operations, Science, and Engineering teams.
  • Design and build automation and tooling that reduce manual effort and improve the consistency and throughput of core data products.

Learn more and apply

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