Agentic AI & Graph Machine Learning Research Engineer at HRL Laboratories
Calabasas, CA
$128,000–$159,950from the description
Jul 28, 2026
Calabasas, CA
Jul 29, 2026
What this job asks for AI summary
A research-focused AI/ML role at HRL Laboratories building agentic AI systems, multi-agent architectures, and knowledge-enhanced AI for national security and commercial applications. The work spans LLM-powered agent frameworks, graph machine learning, knowledge graphs, and trustworthy/explainable AI. Suited to candidates with a strong research background who want to publish, develop novel algorithms, and transition prototype systems into real-world deployment.
Senior level · 3+ years · Los Angeles-Long Beach-Anaheim, CA · Master's required · Secret clearance · Full-time
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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 $132,593 (middle half $93,359–$172,124). This posting is about at that midpoint.
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 (7)
The clearance requirement states candidates must be U.S. citizens with the 'ability to obtain and maintain' a U.S. Government Security Clearance — the specific level is not named. This has been mapped to Secret as the most common baseline for defense/aerospace research roles of this type; the actual required level may differ.
The role blends research science (publishing, novel algorithms, ML modeling) with substantial software engineering (agent frameworks, deployment, distributed systems). 15-2051 Data Scientists was chosen as primary given the research-first framing and emphasis on ML/AI modeling; 15-1252 Software Developers is a genuine runner-up given the depth of engineering and deployment requirements.
The degree requirement is a hard gate: M.S. is the stated minimum. A Ph.D. is listed under Preferred Qualifications.
Real-time streaming and online learning pipelines are listed as optional ('optionally') within the required qualifications block; they have been captured as required given their placement in the Required section, consistent with the 'or similar'/'optional' qualifier rule.
LPG (Labeled Property Graph) and RDF are listed as graph schema examples alongside each other; RDF is emitted as a separate skill since both are distinct standards. LPG is a schema style rather than a named tool and has been omitted.
Parameter-efficient fine-tuning, preference optimization, model alignment, and inference optimization are mentioned as required techniques but name no specific tool or framework, so they are not emitted as discrete skills per the generic-concept rule.
Requires a Secret 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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