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Allen Integrated Solutions

Sensing OPIR Engineer – #897

AI Summary
This onsite engineering role supports the National Geospatial-Intelligence Agency through OPIR and EO/IR sensing analysis, algorithm development, and operational data-processing capabilities. The engineer will prototype and transition advanced processing algorithms, diagnose pipeline anomalies, and contribute technical documentation and transition plans. Required qualifications include an active TS/SCI clearance, a relevant technical degree, at least two years of EO/IR algorithm-development experience, and advanced Python skills. The role demands expertise in infrared phenomenology, OPIR data formats, signal processing, and machine-learning applications. It also requires close collaboration with defense, intelligence-community, industry, and research stakeholders.
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Remote from
USA
Salary
Undisclosed
Department
Software Engineering
Employment
Full Time
Experience
Open level
Published
Apply before
13 Dec 2026
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23
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1
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Opportunity details

About this role.

Remote Sensing OPIR Engineer – #897

Clearance Required: TS/SCI

Minimum Years’ Experience: 2 years of experience in EO/IR algorithm development.

Dayton, Ohio – 100% onsite

Overall Assignment Description:

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Position Overview

Allen Integrated Solutions is seeking a highly skilled Remote Sensing OPIR Engineer to support the National Geospatial-Intelligence Agency (NGA) in infrared sensing, space-based missile warning systems, data exploitation, and algorithm development. The ideal candidate will possess a strong technical background in electro-optical/infrared (EO/IR) systems, infrared phenomenology, Overhead Persistent Infrared (OPIR) data analysis, and remote-sensing software development.

This role requires demonstrated experience working with complex sensor systems, developing and testing advanced analytical capabilities, and collaborating with government, industry, and research partners on next-generation sensing technologies.

Responsibilities

  • Support the prototyping and development of advanced OPIR data-processing algorithms and their transition into operational environments.
  • Evaluate and troubleshoot data-processing pipelines, identify anomalies, and coordinate remediation strategies.
  • Support the development of white papers, technical documentation, and technology-transition plans.
  • Collaborate with research organizations, Department of Defense stakeholders, Intelligence Community organizations, and external agencies.

Required Qualifications

  • Active TS/SCI clearance.
  • Bachelor’s degree in computer engineering, physics, computer science, or a related technical field.
  • At least two years of experience in EO/IR algorithm development.
  • Advanced understanding of EO/IR signal processing, artificial intelligence and machine learning applications, and target-system phenomenology.
  • Detailed experience with OPIR data formats and processing pipelines.
  • Advanced Python development skills.
  • Strong analytical and problem-solving skills, including the ability to diagnose sensor-performance issues, data anomalies, and software problems.

Desired Qualifications

  • Direct experience performing OPIR analysis.
  • Master’s degree in computer engineering, physics, computer science, or a related technical field.
  • Experience collaborating with NGA, NASIC, or other Intelligence Community organizations.

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

4/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThe position combines specialized EO/IR and OPIR domain knowledge with advanced software development in a high-security defense environment. Diagnosing sensor and pipeline anomalies while transitioning algorithms into operations requires strong independent judgment and technical rigor.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Highly competitive
$127,500
US market range (per year)$110k–$145k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer compensation was disclosed. The yearly salary cadence and USD market estimate are inferred for a full-time, onsite US OPIR engineering role requiring an active TS/SCI clearance and specialized EO/IR expertise.

Core skills

Skills and capabilities most closely associated with this opportunity.

EO/IR algorithm developmentOPIR data analysisPythonSignal processingInfrared phenomenologyRemote sensingMachine learningData-processing pipelinesSensor performance analysisAlgorithm prototypingTechnical documentationData anomaly troubleshootingSpace-based missile warning systemsTS/SCI clearance
Sample interview questions
How have you developed or improved an EO/IR processing algorithm?

I begin by defining the operational objective and performance metrics, then characterize the input data, sensor behavior, and known sources of error. I prototype the approach in Python, validate it against representative data sets, measure detection or classification performance, and iterate before packaging the capability for operational use.

How would you investigate an anomaly in an OPIR data-processing pipeline?

I would first determine the anomaly's scope, timing, and operational impact, then inspect data lineage, format validation, processing logs, model inputs, and sensor-health indicators. I would isolate likely failure points, reproduce the issue where possible, implement a corrective action, and document both the root cause and preventive controls.

What factors are important when applying machine learning to EO/IR data?

Key factors include data quality, labeling fidelity, class imbalance, sensor and environmental variability, model explainability, and rigorous validation against mission-relevant scenarios. I would also account for deployment constraints, version control, monitoring, and the ability to retrain or recalibrate models as data conditions change.

How do you transition a research prototype into an operational sensing capability?

I translate research results into clear requirements, create reproducible and tested software components, and validate performance under representative operating conditions. I work closely with users and integration teams to address reliability, interfaces, documentation, security constraints, and sustainment before deployment.

How do you communicate complex technical findings to government and research stakeholders?

I tailor the level of detail to the audience while preserving traceability to the evidence. I use concise technical documentation, clear visualizations, stated assumptions, quantified performance results, and practical recommendations so that both technical and decision-making stakeholders can act on the findings.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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