Job Description

About Kipling Secure

Kipling Secure is a well-funded Silicon Valley startup building a disruptive, AI-driven cybersecurity platform to protect today’s digital infrastructure. Founded by industry veterans from Juniper, Meta, F5, Nokia/Nuage, and leading academic institutions, Kipling is reimagining security with a unique blend of Deep Learning , Generative AI , and Natural Language Processing to deliver intelligent, network-centric threat detection, response, and simplified operations.

Our platform is cloud-native, requires no additional hardware, and is designed to deliver consumer-grade UX with enterprise-grade capabilities—tailored for a wide-range of IT environments including IoT and remote endpoints. We're backed by top-tier venture firms and are moving quickly to bring our product to market.

About the Role

We’re seeking a Principal AI / Machine Learning Engineer to lead the design, development, and deployment of AI models that power our cybersecurity platform. This is a rare opportunity to shape the architecture of a cutting-edge product and work at the intersection of AI, networking, and security.

If you're passionate about building real-world AI systems that solve complex cybersecurity challenges, and want to work with a world-class team, this is your moment.

• Architect and implement end-to-end machine learning pipelines tailored for cybersecurity use cases (EDR/XDR/NDR).
• Develop models that detect, prevent, and remediate sophisticated cyber threats using deep learning, GenAI, and NLP techniques.
• Design and run large-scale experiments to evaluate the effectiveness of AI-driven threat detection strategies.
• Apply modern ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face Transformers) to analyze structured and unstructured security data.
• Work with product, engineering, and threat research teams to translate domain insights into production-grade models.
• Lead technical research and stay current with academic and industry advancements in AI for cybersecurity.
• Contribute to the core ML platform design, including data pipelines, model training/inference infrastructure, and observability.

• 7+ years of experience in machine learning, with at least 3 years focused on applied AI in cybersecurity or infrastructure.
• Deep expertise in ML model development, especially in anomaly detection, time-series analysis, and NLP.
• Experience working with cybersecurity telemetry (e.g., endpoint, network, or cloud data) is highly preferred.
• Strong understanding of EDR/XDR/NDR technologies, networking protocols, and cloud environments.
• Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers.
• Demonstrated ability to take ML models from prototype to production at scale.
• Familiarity with MLOps practices and cloud-native environments (e.g., AWS, GCP, or Azure).
• MS or PhD in Computer Science, Electrical Engineering, Applied Mathematics, or a related field.

• Be part of a founding team building a category-defining product at the forefront of AI and cybersecurity.
• Collaborate with accomplished leaders from the world’s top tech companies and research institutions.
• Influence the technical direction and vision of a rapidly growing startup.
• Work on meaningful challenges that directly impact the safety and security of digital infrastructure worldwide.
• Competitive salary and meaningful early-stage equity in a high-growth company.

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