ML Infrastructure Engineer

ABOUT THE ROLE We are a small, fast-moving enterprise AI infrastructure company (Seed stage, backed by institutional investors) building a context layer that makes AI agents reliable, accurate, and secure for production deployment in regulated industries — including insurance, banking, asset management, healthcare, and logistics. We're looking for a ML Infrastructure Engineer who thrives in early-stage environments and wants to help shape the technical foundation of a product from the ground up. You'll work directly with the founding team, make real architectural decisions, and own critical pieces of a system that handles enterprise data at scale. WHAT YOU'LL DO - Design, build, and maintain end-to-end ML pipelines and production ML systems that power our enterprise context layer. - Fine-tune and deploy Large Language Models (LLMs) and transformer-based architectures for real-world enterprise use cases. - Build and improve information retrieval systems, knowledge graphs, and semantic understanding capabilities across heterogeneous enterprise data sources. - Apply unsupervised learning techniques to discover patterns and relationships in large volumes of unlabeled enterprise data. - Architect and operate large-scale data infrastructure and distributed systems optimized for ML workloads. - Develop and implement NLP solutions including text classification, entity extraction, and semantic understanding. - Own ML model evaluation, monitoring, and optimization in production environments. - Contribute to prompt engineering, retrieval-augmented generation (RAG), and other generative AI techniques. - Drive architectural decisions and set technical direction on high-impact projects alongside a lean, senior founding team. WHAT WE'RE LOOKING FOR Must-haves: - 5+ years of experience as a Machine Learning Engineer building and deploying production ML systems, models, or data pipelines. - Demonstrated experience building and fine-tuning LLMs or working with transformer-based architectures. - Hands-on experience designing and deploying end-to-end ML pipelines in production environments. - Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or equivalent. - Experience with NLP tasks: text classification, entity extraction, semantic understanding, or similar. - Experience building information retrieval systems, search systems, or knowledge graphs. - Experience with unsupervised learning techniques for pattern discovery in unlabeled data. - Experience with large-scale data infrastructure, data lakes, or distributed systems for ML workloads. - Track record of making architectural decisions and owning technical direction in early-stage or high-impact projects. Nice-to-haves: - Experience with prompt engineering, RAG, or other generative AI techniques. - Background in data discovery, data cataloging, or enterprise data management systems. - Prior experience at early-stage startups or founding teams building ML products from scratch. - Experience with ML model evaluation, monitoring, and optimization in production systems. You'll thrive here if you: - Have a founding-team mentality — you're comfortable with ambiguity, move fast, and take ownership end-to-end. - Have production instincts, not just research instincts — you care about systems that work reliably at scale. - Are energized by hard technical problems at the intersection of LLMs, knowledge representation, and enterprise data governance. LOCATION & VISA - Location: On-site in San Mateo, CA. - Visa sponsorship: Available. COMPENSATION & BENEFITS Compensation will be competitive and commensurate with experience, including equity reflecting the early stage of the company. Specific details will be discussed during the interview process.