
Imagery Scientist (SAR)
Required skills
- SAR
- Geospatial
- Imagery Scientist
- Machine Learning
- phenomenology
- image formation
- exploitation products
- metadata
- data format
- data structure
Nice to have
- CV
- CV techniques
- machine learning techniques
Role summary
Leidos is seeking an expert Imagery Scientist specializing in Synthetic Aperture Radar (SAR) to join their team in Alexandria, VA. This role involves providing geospatial and imagery expertise for quantitative analysis to enhance Machine Learning algorithm testing and evaluation. The Imagery Scientist will act as a subject matter expert on SAR, guiding the acquisition and preparation of imagery to meet specific quality standards. Responsibilities include assessing sensor characteristics, metadata, data formats, and developing strategies for data acquisition, pre-processing, standardization, and curation to build diverse datasets for ML model evaluation. The position requires strong analytical skills, experience with SAR phenomenology, image quality metrics, and programming tools like Python and MATLAB.
Leidos has an exciting opportunity for an Expert Level Imagery Scientist (SAR) - to join our team in Alexandria, VA.
The SAR Geospatial Analyst shall provide geospatial and imagery expertise and quantitative analysis to make recommendations that improve data curation and development in support of Machine Learning algorithm testing and evaluation.
The Imagery Scientist is the subject matter expert on their respective imagery modality (e.g., Synthetic Aperture Radar). They shall provide technical direction and conduct the work necessary to acquire and prepare imagery of the necessary quality, standards, and requirements provided by the Government. Developed solutions should be informed by specific phenomenology limitations and advantages of the sensors and platforms in mind.
Primary Responsibilities
- Conduct assessment of potential differences between new sensor characteristics and capabilities compared to currently utilized platforms
- Assess potential differences in metadata, data format, and data structure characteristics in regards to changes to databases, schemas, APIs, and other ETL related processes for the ingestion and movement of data when integrating into the existing data operations pipeline
- Determine how to acquire new data, potential latency associated with acquisition, data formats, and security domains
- Determine how to pre-process and standardize the data to match existing data standards or to be transformed into a usable state for labeling and model testing purposes. This may involve converting between file format types or tiling full-size images into specified sizes or geospatial bounds.
- Investigate any gaps in the emerging sensor capability that may need to be supplemented by other sources
- Explore options for coincident imagery collects from other imagery platforms (e.g., EO platfoms) that align with areas of emerging sensor's collection. Determine other platforms with similar geographic and temporal coverage.
- Leverage multi-INT data, with an emphasis on SAR, to curate imagery from the emergent sensors. Curated imagery will be prioritized for creation of labeled data to support the model evaluation and accreditation. Curated imagery should have objects of interest or needed characteristics (e.g., geographic coverage, scene attributes) per Government prioritization.
· Based on the characteristics and limitations of the emergent sensor, recommend curation strategies and identify data gaps to develop a diverse, representative dataset for Machine Learning model evaluation
· Assess potential differences in metadata, data format, and data structure characteristics in regard to changes to databases, schemas, APIs, and other ETL related processes for the ingestion and movement of data when integrating into the existing data operations pipeline
· Determine how to acquire new data, potential latency associated with acquisition,
Basic Qualifications
- 6+ years as a SAR expert with understanding of collection, phenomenology, image formation process, and exploitation products.
- Bachelor’s Degree in related field. Additional years of experience can be considered in lieu of degree
- Exhibit experience using SARPY and Matlab SAR toolbox
- Experience in RNIIRS, information theoretic-based image quality metrics, SAR imagery quality metrics (e.g., Integrated Sidelobe Ratio, Multiplicative Noise Ratio), and sensor metadata describing impacts of geometry on phenomenology (e.g., graze, squint, azimuth)
- Exhibit experience exploiting SAR to determine the occurrence and location of objects of interest
- Exhibit experience developing, testing, and evaluating new algorithms, processes, methodologies, and products using SAR imagery including experience utilizing advanced processing tools (e.g., Python, MATLAB, Google Earth Engine, etc.) to automate scientific processes of SAR imagery data to aid analysis
- Exhibit experience communicating with a variety of technical and non-technical audiences on availability and capabilities of SAR imagery products, methodologies, procedures, and algorithms to enhance analysis.
- Exhibit a deep understanding of the principles of remote sensing and imagery processing and advanced exploitation methods
- Experience with Synthetic Aperture Radar imagery analytical products and conducting multi-INT analysis
- • Utilize structured, unstructured, and semi-structured data and visualization tools to exploit data through use of programming and scripting, including advanced geospatial skills and an excellent understanding of how to apply evolving technology and methodologies to related issues
- Active TS/SCI clearance with ability to be approved for a Poly
Preferred Qualifications
- Exhibit experience applying CV and machine learning (ML) techniques to SAR imagery and data to address intelligence problems
If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo — because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 — and moving faster than anyone else dares.
Original Posting:
September 23, 2026For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.
Pay Range:
Pay Range $116,350.00 - $210,325.00The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.
Sample Leidos interview questions
- 1
What are the key considerations when designing a highly available and consistent service registry?
system designmedium - 2
Buildings with an Ocean View Find all buildings that have an ocean view. Input: heights = [1,2,3,4] Output: [3] Explanation: Since the ocean is to the right, only the rightmost tallest building (index 3) is not blocked by any others.
codingmedium - 3
Merge K Sorted Linked Lists Merge K sorted linked lists. Input: lists = [[2,2,3],[1,5]] Output: [1,2,2,3,5] Explanation: Extracts the smallest current node iteratively using a priority queue to build a single merged and sorted linked list.
codingmedium - 4
Optimal Meeting Point Calculate the optimal meeting point for multiple people on a 2D grid. Input: grid = [[1,0],[0,1]] Output: 2 Explanation: The optimal meeting point is either (0,1) or (1,0), requiring exactly 1 step from each person resulting in a total distance of 2.
codingmedium - 5
Maximize the minimum distance between aggressive cows in stalls. Input: stalls = [0,4,3,7,10,9], cows = 3 Output: 4 Explanation: Placing the cows at positions 0, 4, and 10 yields a maximum possible minimum distance of 4 between any two cows.
codingmedium
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