Solutions Engineer – AI & Data Science Specialist

This is a fully remote position, open to applicants in Virginia.

📋 Description

• Analyze and interpret outcomes from AI Runtime Security Proofs of Concept (POCs), including red-team initiatives, prompt/response evaluations, and inference-layer assessments.

• Identify false positives and false negatives, providing clear explanations of root causes in a customer-friendly manner.

• Assist in establishing acceptable risk thresholds and success metrics for enterprise AI security implementations.

• Collaborate with clients to enhance prompts, policies, scanner descriptions, and evaluation methodologies.

• Serve as the escalation point for intricate AI behavior inquiries during assessments and pilot projects.

• Facilitate customer workshops centered on AI testing methodologies, evaluation frameworks, and AI risk assessment.

• Transform model behavior and statistical findings into narratives relevant to business (risk, compliance, trust, readiness).

• Contribute to the development of POC readouts, executive summaries, and reports for customers.

• Act as a liaison between Solutions Engineering, Product, and Data Science teams when interpreting scanner performance and model behavior.

• Aid in defining internal best practices for: false positive/false negative analysis, evaluation datasets, prompt and policy refinement, and scanner validation techniques.

• Generate internal guidance, playbooks, and examples to enhance the overall AI literacy of the Solutions Engineering team.

• Offer feedback to Product and Engineering based on real-world customer testing trends.

⛳️ Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, AI, or a related technical discipline.

• Over 5 years of experience in a technical, customer-facing position (Solutions Engineer, ML Engineer, Data Scientist, Applied AI Engineer, or similar).

• Comprehensive understanding of Large Language Models (LLMs).

• Proficient in prompt engineering and evaluation.

• Knowledge of model behavior, biases, and limitations.

• Familiarity with false positive/false negative trade-offs in machine learning systems.

• Experience in analyzing model outputs, classification results, or evaluation metrics.

• Capability to articulate complex AI/ML concepts clearly to individuals without a data science background.

🏝️ Benefits

• Incentive compensation

• Bonus

• Restricted stock units