Senior AI Data Engineer

What are we looking for?

We’re looking for a data scientist who’s deeply motivated to build high-quality datasets that power cybersecurity-focused AI. Your primary mission will be to design and maintain the data foundations behind products that detect threats, summarize alerts, and enable defenders. You should be energized by ambiguity, eager to explore noisy and complex security data, and experienced in transforming it into structured, useful assets.

While your main focus will be on data—collecting, cleaning, labeling, and maintaining—you’ll also help define how we measure quality and track progress over time. We’re especially excited about candidates who understand the nuances of cybersecurity and want to shape how AI is applied to real-world threats.

What will you do?

• Build and maintain datasets that power training, fine-tuning, and prompting of AI models for cybersecurity applications.
• Explore and curate structured and unstructured security data (logs, malware, alerts, incidents) to improve model relevance and effectiveness.
• Develop pipelines for data collection, cleaning, labeling, and versioning.
• Collaborate with researchers and engineers to define data requirements and ensure relevance to cybersecurity problems.
• Contribute to evaluation design and automation to ensure continuous improvement of AI systems.
• Drive data quality and coverage for use cases like summarization, classification, detection, and reasoning over security context.

What skills and experience should you bring?

• 5+ years of experience working with data in applied AI or ML systems, ideally in security or similarly complex domains.
• Strong Python skills and experience with common data tools (pandas, numpy, PySpark).
• Experience curating and maintaining large, evolving datasets for training and evaluation.
• Familiarity with data labeling strategies, data versioning, and pipeline automation.
• Ability to explore and analyze messy data from real-world systems.
• Experience with LLMs or generative AI models, and understanding of their data dependencies.
• A working knowledge of cybersecurity concepts, threat types, telemetry sources, or detection workflows.

Bonus points for:

• Hands-on experience with cybersecurity data (EDR, SIEM, IDS/IPS, threat intel, malware, etc.).
• Familiarity with open-source eval tools (HELM, lm-eval, BIG-bench, etc.).
• Experience with MLOps stacks (Airflow, MLFlow, Weights & Biases).
• Infrastructure skills: cloud platforms, Kubernetes, data pipelines, IaC.
• A passion for building tools that make security professionals more effective.