Principal AI Engineer

Association of Universities for Research in Astronomy•Tucson, AZ

4h•$179,000 - $226,000

About The Position

NOIRLab is seeking an accomplished Principal AI Engineer to define and lead the organization's artificial intelligence strategy, technical architecture, and AI engineering capabilities. This highly visible leadership role will establish the foundational AI platforms, engineering practices, and governance framework that enable scientists, software engineers, and operations teams to harness the power of artificial intelligence in support of groundbreaking astronomical research and observatory operations. As NOIRLab's first dedicated AI engineering leader, you will have a unique opportunity to shape how AI is adopted across one of the world's premier astronomical research organizations. You will identify and prioritize high-impact AI opportunities, build enterprise-scale AI capabilities, and deliver innovative solutions that accelerate scientific discovery, increase engineering productivity, and improve operational efficiency. This role combines strategic vision with hands-on technical leadership. You will partner closely with researchers, software engineers, data scientists, and operational stakeholders to design, prototype, implement, and deploy production-ready AI systems. The ideal candidate is equally comfortable defining long-term AI strategy, architecting scalable AI platforms, and personally contributing to the development of advanced AI solutions. At NOIRLab, we believe scientific discovery and technological innovation go hand in hand. We value intellectual curiosity, continuous learning, and a passion for solving complex problems. This position offers the rare opportunity to apply cutting-edge AI technologies—including machine learning, generative AI, large language models (LLMs), advanced analytics, and agentic systems—to help answer fundamental questions about our universe while advancing the future of scientific computing. If you are energized by the challenge of building AI capabilities from the ground up and motivated by the potential to make a lasting impact on science, this is an exceptional opportunity to lead transformative innovation at a global research institution.

Requirements

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related technical field, or equivalent combination of education and experience.
15+ years of experience in software engineering, artificial intelligence, machine learning, or related fields, including experience serving as a Principal Engineer, AI Technical Lead, Architect, or equivalent senior technical leadership role.
Demonstrated success designing, building, and deploying production-scale AI and machine learning solutions.
Deep expertise in machine learning, generative AI, large language models, modern AI architectures, and AI engineering best practices.
Experience developing and deploying LLM applications, RAG systems, AI agents, and foundation model-based solutions.
Strong background in cloud-native architecture, distributed systems, data platforms, and AI infrastructure.
Experience implementing AI operationalization practices, including model deployment, monitoring, evaluation, governance, and lifecycle management.
Expertise in secure, responsible AI development and model governance frameworks.
Advanced software engineering skills and proficiency with modern programming languages, frameworks, and AI development ecosystems.
Exceptional communication, leadership, and collaboration skills with the ability to influence diverse technical and non-technical stakeholders.

Nice To Haves

Experience applying AI, machine learning, or advanced analytics within scientific research environments.
Familiarity with astronomical datasets, observatory operations, scientific instrumentation, or related research domains.
Experience with high-performance computing (HPC), large-scale data processing, or scientific computing platforms.
Contributions to open-source software, AI frameworks, or scientific computing communities.
Experience building organizational AI capabilities and driving enterprise-wide AI adoption strategies.

Responsibilities

Define and execute NOIRLab's enterprise AI strategy, technical vision, and multi-year roadmap aligned with scientific, engineering, and operational goals.
Identify, evaluate, and prioritize high-value AI use cases that deliver measurable business and scientific impact.
Establish AI engineering standards, development methodologies, reusable frameworks, and best practices that support scalable adoption across the organization.
Assess and implement emerging technologies, including generative AI, foundation models, LLMs, AI agents, and advanced machine learning approaches.
Develop governance frameworks, model evaluation processes, and lifecycle management practices that ensure responsible, secure, and effective AI deployment.
Establish key performance indicators (KPIs) and success metrics to evaluate the effectiveness of AI initiatives.
Design and implement secure, scalable, and production-ready AI platforms and applications that integrate with scientific research systems, software platforms, and operational workflows.
Lead the development of LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, AI agents, copilots, and other advanced AI solutions.
Establish platform capabilities that enable experimentation, model deployment, monitoring, observability, and continuous improvement.
Partner with IT and enterprise technology teams to ensure AI systems meet organizational requirements for reliability, scalability, security, and compliance.
Develop advanced analytics capabilities that support scientific discovery, operational decision-making, and organizational effectiveness.
Apply machine learning, statistical modeling, and AI techniques to large-scale scientific datasets and computational workflows.
Evaluate and optimize model performance, accuracy, explainability, and operational effectiveness.
Collaborate with researchers and technical teams to leverage AI in solving complex scientific and engineering challenges.
Serve as the organization's senior technical authority for AI, machine learning, and advanced analytics.
Mentor engineers, scientists, and technical teams in AI technologies, architecture, and responsible AI practices.
Collaborate across scientific, engineering, and operations organizations to drive AI adoption and innovation.
Engage with external research institutions, observatories, and scientific communities to promote collaboration, interoperability, and knowledge sharing.
Foster a culture of experimentation, continuous improvement, and responsible innovation.