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

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Design, build, and maintain end-to-end ML pipelines and production ML systems that power our enterprise context layer.

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Fine-tune and deploy Large Language Models (LLMs) and transformer-based architectures for real-world enterprise use cases.

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Build and improve information retrieval systems, knowledge graphs, and semantic understanding capabilities across heterogeneous enterprise data sources.

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Apply unsupervised learning techniques to discover patterns and relationships in large volumes of unlabeled enterprise data.

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Architect and operate large-scale data infrastructure and distributed systems optimized for ML workloads.

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Develop and implement NLP solutions including text classification, entity extraction, and semantic understanding.

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Own ML model evaluation, monitoring, and optimization in production environments.

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Contribute to prompt engineering, retrieval-augmented generation (RAG), and other generative AI techniques.

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Drive architectural decisions and set technical direction on high-impact projects alongside a lean, senior founding team.

What We're Looking For
Must-haves:

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5+ years of experience as a Machine Learning Engineer building and deploying production ML systems, models, or data pipelines.

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Demonstrated experience building and fine-tuning LLMs or working with transformer-based architectures.

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Hands-on experience designing and deploying end-to-end ML pipelines in production environments.

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Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or equivalent.

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Experience with NLP tasks: text classification, entity extraction, semantic understanding, or similar.

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Experience building information retrieval systems, search systems, or knowledge graphs.

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Experience with unsupervised learning techniques for pattern discovery in unlabeled data.

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Experience with large-scale data infrastructure, data lakes, or distributed systems for ML workloads.

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Track record of making architectural decisions and owning technical direction in early-stage or high-impact projects.

Nice-to-haves:

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Experience with prompt engineering, RAG, or other generative AI techniques.

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Background in data discovery, data cataloging, or enterprise data management systems.

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Prior experience at early-stage startups or founding teams building ML products from scratch.

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Experience with ML model evaluation, monitoring, and optimization in production systems.

You'll thrive here if you:

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Have a founding-team mentality — you're comfortable with ambiguity, move fast, and take ownership end-to-end.

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Have production instincts, not just research instincts — you care about systems that work reliably at scale.

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Are energized by hard technical problems at the intersection of LLMs, knowledge representation, and enterprise data governance.

Location & Visa

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Location: On-site in San Mateo, CA.

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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.

Originally posted on Himalayas