ML Infrastructure Engineer
About the Role
This is a foundational engineering role at an early-stage AI infrastructure startup building a governed context layer that makes AI agents reliable, accurate, and secure for enterprise deployments in regulated industries. You'll work directly with the founding team, owning the ML systems that sit at the core of the product and shaping the technical direction of the platform from the ground up.
What You'll Do
Design, build, and deploy end-to-end ML pipelines and production ML systems at scale.
Fine-tune and work with Large Language Models (LLMs) and transformer-based architectures to power enterprise AI features.
Build and maintain information retrieval systems, knowledge graphs, and ontology-based data models.
Apply NLP techniques — including text classification, entity extraction, and semantic understanding — to real-world enterprise data challenges.
Architect and manage large-scale data infrastructure and distributed systems supporting ML workloads.
Leverage unsupervised learning techniques to discover patterns across heterogeneous, unlabeled enterprise data.
Evaluate, monitor, and continuously optimize ML models in production environments.
Contribute to architectural decisions and take ownership of technical direction on high-impact, early-stage projects.
What We're Looking For
5+ years of hands-on experience building and deploying production ML systems, models, or data pipelines.
Demonstrated experience fine-tuning or building with LLMs and transformer-based architectures.
Strong proficiency in Python and ML frameworks such as PyTorch or TensorFlow.
Experience with NLP tasks: text classification, entity extraction, or semantic understanding.
Background in building information retrieval or search systems, or working with knowledge graphs.
Familiarity with unsupervised learning methods for pattern discovery in unlabeled data.
Experience operating with large-scale data infrastructure, data lakes, or distributed systems for ML.
Proven ability to make architectural decisions and drive technical ownership in fast-moving environments.
Experience with prompt engineering, RAG, or other generative AI techniques is a plus.
Background in data discovery, data cataloging, or enterprise data management is a plus.
Prior startup or founding-team experience building ML products from scratch is a plus.
Location
On-site in San Mateo, CA. Visa sponsorship is available.