Role Overview

 

The Head of AI Engineering is a technology and entrepreneurial Generative AI engineering leader who will build and scale our new Generative AI and ML Solutions team. You will be responsible for building and executing our Generative AI engineering strategy, governance, and delivery across the organization while aligning them with the organization’s strategic goals of using AI to improve productivity, efficiency, and experience. In this role, you will be at the forefront of the generative AI change, leading a world-class engineering organization that designs and implements cutting-edge solutions to solve Natera’s AI/ML challenges. As part of this role, you will join Natera’s AI office as a key executive member and will be accountable for building and executing an AI solutions delivery plan in alignment with business needs to unlock transformative business value

 

Key Responsibilities

AI Technical Strategy & Architecture

• Own the end-to-end technical vision for the entire AI/ML platform, from data ingestion, MLOps, model serving, fine-tuning, foundation model training, RAG, and agentic applications. 

• Make the critical "build vs. buy vs. open-source" decisions that balance speed, cost, and long-term defensibility

Team Leadership & Talent Development

• Recruit, hire, mentor, and retain an elite team of T shaped AI engineers, applied ML engineers, data scientists, and platform engineers. 

• Design a rigorous hiring process to find "unicorn" talent and foster a culture of continuous learning and excellence.

AI/ML Platform Lifecycle Ownership

• Design, build, and scale an AI/ML platform that provides standardized tooling, infrastructure, and workflows for LLM training, fine-tuning, retrieval-augmented generation (RAG), AI orchestration, and deployment.

• Develop reusable components and services (e.g., vector databases, prompt libraries, agent frameworks, model registries, evaluation pipelines, safety/guardrail modules) to accelerate delivery of AI solutions across product engineering teams.

• Ensure reliability, scalability, and compliance of the AI/ML platform by implementing robust observability, governance, and cost-optimization strategies tailored for large model serving and API consumption.

Execution & Delivery

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