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Remote Sensing Machine Learning Jobs (NOW HIRING)

Booz Allen Hamilton

Machine Learning Research Engineer

$209K/yr

Booz Allen Hamilton is seeking a Machine Learning Research Engineer to support the creation of physics-aware foundational models for remote sensing applications. The role involves training, testing ...

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Remote Sensing Machine Learning information

What are the key skills and qualifications needed to thrive as a Remote Sensing Machine Learning Specialist, and why are they important?

To thrive as a Remote Sensing Machine Learning Specialist, you need expertise in remote sensing principles, image processing, and machine learning algorithms, typically supported by a degree in geoinformatics, computer science, or a related field. Proficiency in programming languages like Python or R, experience with GIS software (e.g., ArcGIS, QGIS), and knowledge of libraries such as TensorFlow or Scikit-learn are commonly required. Strong analytical thinking, problem-solving, and effective communication skills help you interpret data and convey insights to multidisciplinary teams. These skills and qualities are vital for developing accurate models and extracting actionable information from complex geospatial datasets.

What are some common challenges faced when developing machine learning models for remote sensing data?

Developing machine learning models for remote sensing often involves handling large, complex, and multi-dimensional datasets, such as satellite imagery or LiDAR point clouds. Common challenges include dealing with data quality issues like cloud cover, varying resolutions, and sensor noise. Additionally, remote sensing data frequently requires significant preprocessing and annotation, and model generalization can be difficult due to geographic variability. Collaborative work with domain experts in geospatial science and regular communication with data engineers is crucial to address these challenges effectively.

What is the difference between Remote Sensing Machine Learning vs Geospatial Data Analyst?

AspectRemote Sensing Machine LearningGeospatial Data Analyst
Required CredentialsBachelor's or higher in GIS, remote sensing, computer science; certifications in machine learning or GISBachelor's or higher in GIS, geography, or related field; certifications in GIS or data analysis
Work EnvironmentResearch labs, tech companies, government agencies; focus on developing algorithms and modelsUrban planning, environmental agencies, consulting firms; focus on data interpretation and reporting
Industry UsageRemote sensing, AI, machine learning, geospatial techGIS, urban planning, environmental management, data visualization

Remote Sensing Machine Learning specialists focus on developing algorithms to analyze satellite and aerial data, often working in tech or research environments. Geospatial Data Analysts interpret and visualize geospatial data for practical applications. While both roles require GIS knowledge, Remote Sensing Machine Learning emphasizes algorithm development, whereas Geospatial Data Analysts focus on data interpretation and reporting.

What are remote sensing machine learning jobs?

Remote sensing machine learning jobs involve applying machine learning algorithms to analyze data collected from remote sensing sources, such as satellites, drones, or airborne sensors. Professionals in this field process and interpret large-scale geospatial data to extract meaningful patterns, detect changes, and produce actionable insights for applications like agriculture, disaster management, environmental monitoring, and urban planning. These roles typically require expertise in image processing, data analysis, programming, and an understanding of both remote sensing technologies and machine learning techniques.
Physicist / Remote Sensing Scientist

Physicist / Remote Sensing Scientist

Barone Consulting

Dayton, OH • Remote

Full-time

Posted 15 days ago

Job description

We are seeking an experienced TS/SCI cleared Remote Sensing Scientist / Physicistwith a deep understanding of the mathematics/geometry of sensor homography / image stitching. The ideal candidate will have photogrammetry skills and a background with remote sensing principles and sensor/target phenomenology or spectral phenomenology and algorithm development experience across a broad spectral range from visible to thermal. If your strength lies in domain image processing and data/scene exploitation and you want to work on a compelling project that has to do with enabling next-generation capabilities for national defense, specifically to help shape the future of airborne and space-based surveillance, this position will place you at the cutting edge.You will support a large R&D program that is focused on providing advanced intelligence capabilities for the benefit of the DoD and IC communities.

You will be part of a very high visibility project on a mid-sized team where you will work with seasoned scientists and developers, creating solutions to complex problems within a collaborative team environment.ResponsibilitiesDevelop and apply advanced models for sensor homography, image stitching, and geometric alignment of multi-sensor and/or multi-modal imageryAnalyze remote sensing data using domain-specific knowledge of sensor and target phenomenology (OPIR, EO, MSI, etc)Implement computer vision and image processing techniques for large-scale scene exploitationSupport development and analysis of persistent collection systems and architecturesConduct technical studies involving focal plane array characteristics, such as quantum efficiency, flux density, pixel crosstalk, and noise modelingUtilize Python and/or MATLAB for prototyping, algorithm development, and data analysis to write scientific code for clutter and noise mitigation, image reconstruction, compressive sensing, tracking, detection, feature extraction and classification, stereo, machine learning, signal processing, and radiometric calculationsCollaborate on multidisciplinary R&D efforts and contribute to technical strategy developmentRequired Must Have SkillsIn-depth understanding of mathematical principles behind image registration, sensor fusion, and geometric modelingStrong grasp of scientific focal plane array (FPA) concepts, including quantum efficiency, crosstalk, pixel sensitivity, and flux behaviorFamiliarity with persistent collection systems, their design considerations, and operational implicationsDemonstrated expertise in domain-specific remote sensing, including sensor/target interactionsProficiency in both Python and MATLAB and for scientific computing and algorithm developmentExperience developing or optimizing algorithms for deployment in operational environmentsActive TS/SCI clearance with CI Scope PolygraphExperience designing and deploying computer vision algorithms in remote sensing workflowsKnowledge of spectral imaging, radiometric calibration, or sensor characterizationWorking familiarity with AI/ML techniques applied to spatial or temporal imageryRecord of published work or conference presentations in the fields of remote sensing or sensor modelingExperience transitioning algorithms from prototype to deployed system in defense or ISR environmentsQualifications (education/experience substitutions are allowed)Mid-level candidates must have a B.A./B.S. and 4 years of experienceSenior level candidates must have a B.A./B.S. and 10 years of experienceSME level candidates must have a M.A./M.S.

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