[Hiring] Senior Machine Learning Engineer @Carbon Mapper

Role Description

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.

Qualifications

• 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.
• 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.

Requirements

• 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.

Location

Carbon Mapper is headquartered in Pasadena, California, but we embrace the virtual office and are continuing to grow our team across the United States.

Compensation

The 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.

Benefits

• Affordable health coverage (medical, dental, vision).
• A generous time-off policy.
• Professional development.
• And more.

Company Description

Carbon 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.