Head of AI

Critical Materials Group•Austin, TX

1d•Hybrid

About The Position

The Head of AI will establish CMG's artificial intelligence and machine learning capability for advanced manufacturing and operations. This is initially a hands-on individual contributor role responsible for architecting and building the enterprise AI technology foundation. Working with various department heads (operations, production, development, growth and people), you will translate needs (known and unknown) into secure integrated AI capabilities that improve decision making and automated workflows across an AI technology stack that you build. As the capability matures, this leader will develop a small team and expand the platform toward physical automation and agentic systems that support rapid, reliable production scale-up. You will be pivotal to establishing security standards, governance, and performance to scale AI adoption to improve energetics manufacturing and business operations in order to make CMG the premier data and technology company automating defense energetics manufacturing.

Requirements

Bachelor's degree in computer science, artificial intelligence, machine learning, data science, engineering, or a closely related quantitative field, or equivalent demonstrated experience.
At least five years of relevant experience in machine learning, applied AI, data engineering, industrial analytics, or intelligent automation.
Experience architecting and building enterprise AI capabilities from strategy development, infrastructure stand-up, and building agentic tools/capabilities.
Hands-on experience selecting, adapting, training, evaluating, and deploying machine learning or foundation models for real-world applications.
Strong understanding of open-source model ecosystems and the tradeoffs among open, proprietary, and custom model approaches.
Experience designing data pipelines, schemas, ontologies, or comparable structures that convert unstructured, raw data into reliable model-ready data.
Demonstrated experience applying AI, machine learning, or advanced analytics in manufacturing, industrial operations, or another physical production environment.
Working knowledge of sensors, instrumentation, industrial control systems, or manufacturing automation sufficient to collaborate effectively with controls and equipment specialists.
Ability to move between strategy and implementation, including writing code, evaluating data, prototyping solutions, and testing them against physical processes.
Ability to communicate clearly and work effectively with engineers, scientists, operators, executives, and external partners.
United States citizenship and the ability to satisfy applicable export control, background, security, and site-access requirements.
Ability and willingness to travel approximately 40%.

Nice To Haves

Experience with continuous-flow chemistry, pharmaceutical manufacturing, specialty chemicals, materials processing, or another liquid-to-solid production environment.
Experience with physical AI, robotics, machine vision, automated material handling, pick-and-place systems, or automated assembly.
Experience applying AI or automation in automotive, aerospace, space, semiconductor, cementitious-material, or other advanced manufacturing environments.
Experience with process optimization, anomaly detection, predictive quality, computer vision, reinforcement learning, or agentic workflows.
Experience working with sensitive, proprietary, export-controlled, or classified data and computing environments.
Experience establishing a new technical capability, moving from prototype to production, and building a small high-performing team.
Advanced degree in a relevant technical field.

Responsibilities

Define and execute CMG’s AI/ML strategy, technical foundation, investment priorities, and phased implementation roadmap. Communicate technical decisions, risks, progress, and resource requirements to executives and technical stakeholders outside the AI function.
Partner with operations, production, programs, growth, safety, and external partners to translate business needs and critical process parameters into technical requirements and implementation plans. Evaluate sensors, instrumentation, and data acquisition approaches, establishing reliable, traceable methods for measuring, collecting, storing, structuring, and accessing data.
Develop practical tools that use manufacturing data to improve process safety, product quality, operational visibility, and production efficiency.
Evaluate open and closed AI models for chemistry, synthesis, formulation, industrial processes, and manufacturing automation. Select approaches appropriate for proprietary, export-controlled, and security-sensitive information.
Build, adapt, and validate models using CMG data, with clear performance metrics, evaluation methods, and controls for safe deployment. Establish secure development practices, technical documentation, model governance, and enterprise data-handling standards appropriate for ITAR-controlled and proprietary manufacturing environments.
Develop a phased path from monitoring and decision support to closed-loop optimization, AI-enabled process control, and agentic systems.
Advance physical AI and automation applications across continuous-flow synthesis, energetic material formulation, material handling, automated assembly, load-assemble-pack operations, inspection, and quality verification. Support rapid iteration between software and equipment at CMG production and pilot-plant facilities and with university and technical partners.
Initially serve as a hands-on builder and individual contributor, then identify capability gaps, recruit complementary talent, and lead an AI and data team expected to grow to approximately three to five people.
Within the first six months, establish CMG’s data strategy and AI-ready data foundation, lead development of an enterprise ontology across departments, and prioritize AI use cases with measurable business outcomes.
Produce an enterprise AI and data strategy with associated phased roadmap for implementation.
Begin capturing currently available equipment, process, and visual workflow data that is not yet collected consistently.
Create a clean and usable data architecture, including common definitions, metadata, and relationships among process information.
Establish baseline measures for safety, quality, cycle time, throughput, and process efficiency.
Deliver initial analytical or machine learning tools that demonstrate measurable operational value.
Recommend the model stack and secure computing approach CMG should use for proprietary and controlled information.