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 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. Key Responsibilities 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. What We're Looking For 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. Why Join Kipling Secure 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. Seniority level Seniority level Mid-Senior level Employment type Employment type Full-time Job function Job function Engineering and Information Technology Referrals increase your chances of interviewing at Unusual Ventures by 2x Get notified about new Machine Learning Engineer jobs in San Jose, CA . 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