Senior AI/ML Engineer

NLP and Vulnerability Intelligence

• Own the NLP-based CWE normalization module that maps heterogeneous SAST, DAST, and SCA finding schemas to a canonical CWE taxonomy, identified as the highest-ROI AI addition in the current roadmap
• Design and train text classification and entity extraction models for vulnerability description normalization across tools with inconsistent output formats
• Build and maintain embedding pipelines for vulnerability fingerprinting, similarity detection, and cross-source deduplication
• Develop persistent organizational vulnerability memory using vector retrieval, including suppression logic and threat-condition-triggered resurfacing

Bayesian Scoring and Prioritization

• Design and own the Bayesian confidence scoring layer that combines CVSS, reachability signals, exploit availability, and business context weighting into a single actionable priority score
• Define, track, and continuously improve against accuracy targets: above 92% correlation accuracy, above 85% priority rank accuracy, below 3% WAF false positive rate
• Build calibration and evaluation frameworks so scoring outputs remain explainable and auditable, not black boxes
• Research and incorporate threat intelligence signals and exploit likelihood indicators as scoring features

Reachability and Static Analysis Integration

• Build the reachability gate that filters SAST findings through callgraph and data-flow signals, targeting 60 to 75% noise reduction without suppressing true positives
• Define the integration contract between static analysis outputs and the ML pipeline, enforcing hard constraints such as SAST-only signals never triggering WAF rule generation
• Collaborate with the AppSec integration layer to ensure finding schemas from different source categories are normalized correctly before entering the ML pipeline

Agentic AI Systems

• Architect and build agentic workflows where LLMs perform multi-step vulnerability triage, generate fix suggestions, and cross-validate findings across SAST and DAST sources
• Design the virtual patch generation pipeline: from a correlated, scored vulnerability signal to a WAF rule proposal, including confidence thresholds that gate human approval requirements
• Build the autonomous remediation agent architecture using MCP server infrastructure, with human approval gates enforced at the system level rather than the prompt level
• Define prompting strategies, output schemas, and evaluation harnesses for LLM-generated security content where correctness is non-negotiable
• Drive the product goal of engineers completing full triage and response workflows through agentic conversational interfaces

ML Operations and Quality

• Instrument the full ML pipeline with evaluation metrics, drift detection, and feedback loops from human approval decisions
• Build offline evaluation datasets from historical vulnerability findings to benchmark model changes before production deployment
• Define the model routing strategy across LLMs of varying capability and cost, applying frontier models where fix quality matters and lighter models for triage throughput