Carbon Mapper uses advanced imaging spectroscopy remote sensing on satellites and airborne assets to detect and analyze methane and carbon dioxide emissions around the world and provide information data products to inform decision makers as well as the general public. As part of its public education and research support activities, Carbon Mapper will provide a global open data portal, including a data trust and a website offering rapid visualization and interpretation for both expert and non-expert audiences.
Carbon Mapper also supports advances in science algorithms, machine learning, and big data for remote sensing through multi-source analytics, cloud computing, open source development, Amazon Web Services, and geospatial analytics.

Carbon Mapper

Driving greenhouse gas emission reductions by making methane and carbon dioxide data accessible and actionable.

About Carbon MapperCarbon Mapper is a non-profit organization based in Pasadena, CA with the mission to drive greenhouse gas emission reductions by making methane and carbon dioxide data accessible and actionable. We leverage remote sensing technology to detect, pinpoint, and quantify methane and carbon dioxide (CO2) emissions at the scale of individual facilities. All of our methane and CO2 data is made publicly available for non-commercial use on our Carbon Mapper Data Portal to provide decisionmakers with the information they need to prioritize and take mitigation action.
Carbon Mapper also works with stakeholders and decision makers to fill data gaps, lead on cutting edge science, collaborate to drive reductions, and advance education and insights on emissions globally. To do this, we work with partners to leverage a constellation of satellites. Data from these satellites will offer the next major step in scaling up the organization's robust data portal featuring thousands of direct observations of global methane and CO2 super-emitters.
To learn more about Carbon Mapper, please visit us at https://carbonmapper.org.To view our data, please visit us at https://data.carbonmapper.org.
About The RoleAs 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 ResponsibilitiesDesign, 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.
Minimum Qualifications (Knowledge, Skills, and Abilities)Must-Have Skills & Experience:Advanced degree or equivalent experience in Earth Science, Atmospheric Science, Remote Sensing, Computer Science, Statistics, Artificial Intelligence, or a related field.Experience building statistical and machine learning models using remote sensing datasets, with applied expertise in deep learning approaches (e.g., transformers) and frameworks such as PyTorch, TensorFlow, or similar.Demonstrated ability to use remote sensing imagery in deployed ML systems for detection of atmospheric gases, weather conditions, natural or agricultural ecosystems, or land use/land cover.Comfort adopting AI tools across the ML workflow, including AI-assisted coding and AI-supported model development, testing, and evaluation, with a clear perspective on when and how these tools add value and where careful review and engineering judgment are needed to verify outcomes.Experience designing and evaluating production ML systems, defining model performance metrics and validation strategies that reflect real operational conditions.Track record of diagnosing model errors, bias, drift, and failure modes in production, and translating those findings into concrete model or pipeline improvements.Experience deploying, monitoring, and maintaining models in production, with a focus on observability, reproducibility, and sound model-lifecycle practices.Experience developing scalable training and inference pipelines that balance performance, latency, and cost.Ability to contribute to and justify ML architecture and build-versus-buy decisions, and to lead significant experimentation efforts.Commitment to model explainability, with the practical ability to make models, features, and decision processes interpretable to both technical and non-technical stakeholders.Solid Python programming skills across the scientific and ML stack (NumPy, SciPy, scikit-learn, etc.), with the ability to apply standard software development practices and clearly document results.Track record of influencing stakeholders and collaborating with peers across engineering, product, and science functions, with strong written and verbal communication skills and the ability to present clearly to both technical and non-technical audiences.
Nice-to-Have Skills:Background in atmospheric or weather remote sensing applications, including methane plume detection, detection of trace gases, or weather modeling.Familiarity with cloud-native geospatial data formats and catalogs (STAC, Cloud-Optimized GeoTIFF, Zarr) and large-scale raster/hyperspectral processing.Experience deploying and operating ML systems in scalable cloud environments (GCP, AWS, or Azure), including containerization (Docker/Kubernetes) and data pipelines that balance quality, latency, and cost at scale.Solid understanding of MLOps practices and demonstrated experience implementing them in production.
LocationCarbon Mapper is headquartered in Pasadena, California, but we embrace the virtual office and are continuing to grow our team across the United States.
CompensationThe total compensation for this opportunity includes a base salary range of $155,000 – $185,000. This is our target compensation range and is subject to multiple factors including level, experience, and location. As you go through our interview process, our recruiter will work with you to identify a competitive base salary within the proposed range.
BenefitsAs a Carbon Mapper employee, you will have access to a wide range of benefits including affordable health coverage (medical, dental, vision), a generous time-off policy, professional development, and more.
To apply, please visit us at https://carbonmapper.org/about/careers
Carbon Mapper is an equal opportunity employer that believes a diverse team will not just lead to a better working environment, but will also help us create better products and services.
Carbon Mapper is committed to the full inclusion of all qualified individuals. If any reasonableaccommodation is needed in our process, please reach out to hr@carbonmapper.org.