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F5

Solutions Engineer – AI & Data Science Specialist

F5

Solutions Engineer specializing in AI & Data Science at F5. Analyzing AI security POCs and collaborating with customers on evaluation strategies.

Posted 5/15/2026full-timeRemote • Virginia • 🇺🇸 United StatesMid-LevelSenior💰 $123,500 - $185,300 per yearWebsite

About the role

Key responsibilities & impact
  • Analyze and interpret results from AI Runtime Security POCs, including red-team campaigns, prompt/response scans, and inference-layer inspections.
  • Diagnose false positives and false negatives, explaining root causes in clear, customer-friendly language.
  • Help define acceptable risk thresholds and success criteria for enterprise AI security deployments.
  • Partner with customers to refine prompts, policies, scanner descriptions, and evaluation strategies.
  • Act as the escalation point for complex AI behavior questions during evaluations and pilots.
  • Support customer workshops focused on AI testing methodology, evaluation frameworks, and AI risk interpretation.
  • Translate model behavior and statistical outcomes into business-relevant narratives (risk, compliance, trust, readiness).
  • Assist in shaping POC readouts, executive summaries, and customer-facing reports.
  • Serve as the bridge between Solutions Engineering, Product, and Data Science when interpreting scanner performance and model behavior.
  • Help define internal best practices for: FP/FN analysis, Evaluation datasets, Prompt and policy tuning, Scanner validation strategies.
  • Create internal guidance, playbooks, and examples to raise the overall AI literacy of the SE team.
  • Provide feedback to Product and Engineering based on real-world customer testing patterns.

Requirements

What you’ll need
  • Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, AI, or a related technical field.
  • 5+ years of experience in a technical, customer-facing role (Solutions Engineer, ML Engineer, Data Scientist, Applied AI Engineer, or similar).
  • Strong understanding of Large Language Models (LLMs)
  • Prompt engineering and prompt evaluation
  • Model behavior, bias, and limitations
  • False positive / false negative tradeoffs in ML systems
  • Experience analyzing model outputs, classification results, or evaluation metrics.
  • Ability to explain complex AI/ML concepts clearly to non-data-scientists.

Benefits

Comp & perks
  • Incentive compensation
  • Bonus
  • Restricted stock units

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools
AI Runtime SecurityLarge Language Modelsprompt engineeringmodel behavior analysisfalse positive analysisfalse negative analysisevaluation metricsstatistical outcomes interpretationrisk assessmentcustomer-facing reporting
Soft Skills
communicationcustomer collaborationproblem-solvingtechnical explanationworkshop facilitationnarrative translationbest practices developmentfeedback provisionteam collaborationrisk interpretation
Certifications
Bachelor’s degreeMaster’s degree