Lead AI Engineer (Computer Vision & Fusion)
Oslitandi Tech LLC · Washington, DC
$208,000–$260,000
Jul 28, 2026
Washington, DC
Jul 29, 2026
What this job asks for AI summary
This role centers on designing, training, and optimizing deep learning models for object detection, classification, and tracking using multi-modal sensor data — including optical, SAR, radar, and EO/IR inputs — in a defense or national-security context. The engineer will lead sensor fusion algorithm development, drive edge-compute model optimization for sub-10ms inference, and oversee the full ML lifecycle into an MLOps pipeline, while mentoring junior engineers on the AI/ML team.
Senior level · 7+ years · Master's required · TS/SCI clearance
“or” means any one of them counts — you don't need all of them.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $122,874 (middle half $87,544–$162,374).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 29, 2026. It is a model, not a headcount.
Why we read it this way (8)
No work location is specified in the posting; the TS/SCI clearance requirement strongly implies an on-site or cleared-facility position in the US, so remote is set to false and the US-based flag is set to true, but the specific metro is unknown.
The degree requirement is a hard gate: the posting states 'The candidate shall have a Master's or PhD' — Masters is set as the minimum.
The TS/SCI clearance is stated as a hard eligibility gate ('Must be eligible for a U.S. Government TS/SCI Clearance'), not merely a preference.
PyTorch is listed as preferred over TensorFlow within the required frameworks section; TensorFlow is captured as an alternative since either satisfies the gate.
YOLO and Faster R-CNN are presented as illustrative examples of required detection methodology experience; both are captured with YOLO as primary and Faster R-CNN as alternative.
Model pruning, quantization, and MLOps appear in the responsibilities narrative rather than a discrete qualifications list, so they are marked preferred rather than hard gates.
The SOC is Medium confidence: the role is deeply research-oriented (ML/AI, statistics, modeling) pointing to 15-2051, but the volume of software engineering and systems work (edge deployment, containerization, MLOps pipeline) makes 15-1252 a plausible runner-up.
Requires a TS/SCI clearance — the cleared population is a small fraction of this occupation, so the real candidate pool is materially smaller than the estimate below, which does not model clearance.
Read the full posting
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