Security AI ML Engineer: Build Safe, Scalable LLMs

Witness AI; Mountain View (CA); On-site; USD 36,000 - 60,000; Full time

Job summary

Witness AI in Cairo seeks a Machine Learning Engineer to design, build, and evaluate language models powering our AI security products. You’ll own the end-to-end pipeline from data curation to experiment design and visualization of results.

You will collaborate with researchers and data scientists to translate ideas into engineering workflows, fine-tune models, and integrate them into production with CI/CD, monitoring, and robust guardrails.

Qualifications

• 2–5+ years of ML or data science experience, preferably in a security or infrastructure-heavy setting.
• Strong knowledge of ML fundamentals and evaluation metrics.
• Experience with LLMs, safety, and model robustness is a plus.

Responsibilities

• Build scalable pipelines to collect, preprocess, and manage datasets for training and evaluation of LLMs.
• Design and run experiments to evaluate LLMs on accuracy, robustness, fairness, and safety.
• Create dashboards, reports, and visualizations to communicate evaluation results, trends, and failure cases.
• Develop and leverage knowledge graphs to structure data, enrich evaluation, and improve context-driven model performance.
• Work with researchers to translate new ideas into engineering workflows, and with data scientists to automate QA checks and guardrails.
• Fine-tune, optimize, and integrate models into production systems with a focus on reliability, scalability, and monitoring and CI/CD best practices.
• Contribute to ML tooling and experimentation frameworks to accelerate iteration.

Skills

ML experience

Security aware

Startup mindset

Experimentation

Tools

CI/CD tooling

Knowledge graphs

Job description

Witness AI in Cairo seeks a Machine Learning Engineer to design, build, and evaluate language models powering our AI security products. You’ll own the end-to-end pipeline from data curation to experiment design and visualization of results.

You will collaborate with researchers and data scientists to translate ideas into engineering workflows, fine-tune models, and integrate them into production with CI/CD, monitoring, and robust guardrails.