Machine Learning Engineer

About the Role: Develop and Manage AI/ML Infrastructure and Lifecycle: Build and maintain scalable and reliable AI/ML infrastructure to support model deployment, monitoring, and optimization. Contribute to the full AI/ML lifecycle—from development to production—while implementing observability tools to collect data, ensuring models perform reliably and enabling data-driven improvements. Design, Develop, and Deploy Models (especially LLMs): A part of your role will focus on integrating and deploying foundational generative AI models, such as LLMs, using techniques like retrieval-augmented generation (RAG). You may also develop additional models to complement and interact with these foundational models, with potential for fine-tuning based on specific use cases. This includes understanding requirements, designing solutions, collaborating with data engineers for necessary data, and deploying models into production. You will also be responsible for developing other models as needed to support evolving product requirements. Collaborate within a Cross-Functional Team: Work closely with product managers, product developers, threat analysts, data scientists, and other engineers to understand the business requirements and translate them into ML solutions. Keep Pace with Research: Keep up with the latest research in machine learning, large language models, and cyber security and find ways to implement new findings into our core technology. What You Have: Most importantly, a passion for ensuring data quality, observability, and model performance through effective monitoring, validation, and data collection practices. Masters/PhD (or Bachelor’s with equivalent experience) in computer science, statistics, mathematics, or related field, or equivalent work experience 2+ years of experience in data science, machine learning, or data analytics Proficient in one or more programming languages, such as Python, R, or Scala Experience with machine learning frameworks and libraries, such as TensorFlow, PyTorch, or Scikit-learn Experience with cloud platforms and services, such as AWS, Azure, or GCP Bonus Points: Experience training and fine-tuning large-scale foundational models, such as LLMs, or developing other production-grade language models, with a strong understanding of managing AI infrastructure and projects in a DevOps/MLOps setting. Familiarity with security concepts, tools, and solutions, such as threat detection, vulnerability scanning, encryption, or authentication.

Andesite is building the next generation cybersecurity analyst experience. Our mission is to supercharge the analysts protecting our country’s networks. When analysts work in our advanced AI security analytics platform, they can analyze decentralized data sets at scale and more quickly respond to threats. They become better and smarter, just by doing the things they’re already built to do. We have deep experience with this problem. Our team is born out of the security community and comes from a diverse range of backgrounds and experiences including the CIA, NSA, military, big tech, and start ups. After raising a $15M seed round from top-tier investors General Catalyst and Red Cell Partners, we are rapidly scaling. As we remain hyper focused on our growth path and our accelerated move to market, we believe having the right teammates in Andesite Nation will be the reason we win.