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.