• Design, develop, and deploy machine learning and deep learning models for scientific applications (e.g., materials discovery, chemical modeling, process optimization) • Translate complex scientific and business problems into tractable AI/ML frameworks • Work with real-world structured and unstructured scientific data (e.g., experimental, simulation, and literature data) • Build and maintain data pipelines, feature engineering workflows, and model evaluation frameworks • Collaborate cross-functionally with scientists, engineers, and product managers to deliver production-ready solutions • Partner closely with Customer Success and client-facing teams to understand customer needs, translate requirements into AI/ML solutions, and support the successful deployment, adoption, and ongoing optimization of models in customer environments • Apply techniques such as supervised/unsupervised learning, generative models, and optimization algorithms • Contribute to the integration of models into scalable software platforms and APIs • Stay current with advancements in AI/ML and relevant scientific domains; evaluate and apply new methods where appropriate • Perform strategic research oriented towards improving NobleAI’s core technology • Communicate findings and model outputs clearly to both technical and non-technical stakeholders • Periodic travel to customer sites and attend industry events

Role Description We are seeking a Machine Learning Intern to join our innovative team. This internship will focus on applying advanced computer vision, language models, and reinforcement learning to complex, domain-specific architectural and engineering data. The role offers the unique opportunity to work on cutting-edge problems at the intersection of AI, spatial reasoning, and the AEC industry, helping shape the future of automated design and structural analysis. Location: Austin, TX or New York, NY - Primary preference for Austin or NYC; remote-eligible for exceptional candidates with a proven track record. Core Responsibilities - Research and prototype computer vision models and language models tailored for domain-specific tasks, such as understanding and processing architectural plans, engineering documents, and building system data. - Develop robust data pipelines for curating, training, and fine-tuning models on diverse engineering data, including 2D drawings, 3D geometries, and text-based specifications. - Implement machine learning algorithms for tasks such as object detection, semantic segmentation, and advanced reasoning within the AEC domain. - Explore and implement reinforcement learning frameworks to optimize and automate complex decision-making processes in the built environment. - Collaborate with the engineering team to deploy AI models into our core design and analysis workflows, applying MLOps best practices for scalable machine learning deployment. - Conduct rigorous experiments and evaluate model performance on real-world AEC use cases to ensure scalability and accuracy. Qualifications - Currently enrolled in a graduate or undergraduate program in Computer Science, Engineering, Machine Learning, Applied Mathematics, or a related field. - Strong proficiency in Python, with a solid foundation in deep learning and hands-on experience using frameworks like PyTorch or TensorFlow. - Familiarity with core machine learning concepts and techniques, including supervised/unsupervised learning, model training and evaluation, and common architectures (e.g., CNNs, GNNs, transformers). - Demonstrated research experience, such as publications, preprints, conference submissions, or substantive research projects, with the ability to read, critique, and build on recent ML literature. Bonus Qualifications - Experience with any of vision, language, and graph neural networks or 3D/geometric deep learning, including CNNs (U-Net, ResNet), GNNs, and NeRFs. - Familiarity with modern vision, language generative models (e.g., VAEs, Diffusion, Transformers, ViTs, Multimodal models). - Knowledge of reinforcement learning principles and frameworks as applied to optimization or decision-making problems. - Background in Engineering, Architecture, or AEC with hands-on experience processing complex engineering data or spatial representations (CAD/BIM), and familiarity with relevant software (e.g., SAP2000, ETABS, Revit), reinforced concrete/steel design, and building codes (e.g., ASCE 7, ACI 318, AISC 360). - Experience with MLOps practices, including experiment tracking, model deployment, or working with large-scale datasets and distributed training. How we operate - Ownership & Intensity: We operate with high autonomy and a shared sense of urgency to redefine an entire industry. - Bias for Action and Efficiency: We prioritize automation over manual effort to solve high-consequence problems, shipping quickly and often. - Data-Driven Decisions: Hedral’s core advantage is not just automation, but that our system learns from real, stamped engineering work. Every project feeds back into the model. Benefits - Competitive Compensation and Benefits. - Environment for growth. - Engineering Redefined: Hands-on experience with the future of automated design that goes beyond traditional manual workflows. - Mentorship & Velocity: Fast-track career development working alongside industry leaders in both structural engineering and software development.

• Serve as EnerSys’ executive leader and single point of accountability for enterprise AI strategy, execution, and outcomes. • Translate enterprise and business strategies into a clear, multi‑year AI vision and roadmap aligned with growth, productivity, quality, safety, and risk objectives. • Advise the CIO, ELT, and Board on AI trends, risks, opportunities, and competitive implications. • Represent EnerSys externally with strategic AI partners, technology providers, and industry forums. • Design, implement, and continuously evolve the enterprise AI operating model, including governance forums, funding and intake processes, prioritization methods, and decision rights. • Chair and run the Executive AI Steering Committee, ensuring disciplined decision making, clear accountability, and execution follow through; set agendas, align key stakeholders, and communicate decisions and actions clearly. • Establish enterprise standards for responsible AI, data usage, security, privacy, and risk management in partnership with Legal, Cybersecurity, IT Architecture, HR, and Compliance. • Own AI investment governance, ensuring alignment with capital planning, transformation funding, and enterprise portfolio priorities. • Own the global AI use case portfolio and prioritization framework, balancing short term value delivery with long term strategic capability building. • Ensure AI initiatives progress efficiently from ideation to pilot to scaled production—eliminating “pilot only” stagnation. • Define, track, and report enterprise AI value metrics, including operational efficiency, cost reduction, quality improvement, risk mitigation, revenue enablement, and customer impact. • Regularly recalibrate the AI roadmap based on realized outcomes, business performance, and evolving priorities. • Orchestrate cross-functional delivery across IT, data, digital platforms, business units, and external partners—creating shared plans, clear ownership, and fast decisions. • Resolve enterprise level dependencies and escalations impacting delivery, adoption, or value realization by facilitating trade-off discussions, aligning decision makers, and driving timely, documented outcomes. • Ensure AI initiatives are integrated with core enterprise platforms (ERP, data environment, digital products) rather than delivered as isolated solutions. • Build scalable execution playbooks, delivery standards, and reusable components to accelerate adoption across regions and functions. • Partner with HR and Communications to lead enterprise-wide change management, AI literacy, and workforce enablement. • Sponsor AI capability building, including training programs, citizen developer models, communities of practice, and leadership education. • Drive cultural adoption by positioning AI as a core business capability embedded in how work gets done—not as a technology experiment. • Support workforce transformation planning, including role evolution, ethical considerations, and future skills development. • Build, lead, and develop a high performing enterprise AI organization with clear roles, succession, and career paths. • Set performance expectations, accountability, and development plans for direct and indirect reports. • Foster a strong culture of accountability, collaboration, and execution discipline across the AI ecosystem.

Role Description We are seeking an Applied AI/ML Scientist that will be responsible for developing and deploying machine learning models that solve complex, real-world scientific and industrial challenges. In this role, you will combine advanced AI/ML approaches with your energy sector expertise to build models that solve complex industrial problems, optimize processes, and drive real-world breakthroughs. You will work closely with domain experts, product teams, and engineers to translate scientific problems into scalable AI solutions that drive measurable business and customer impact. If you have a passion for solving industry challenges with real impact, let’s talk! Join us in building a more sustainable world through the power of AI and scientific innovation. Key Responsibilities - Design, develop, and deploy machine learning and deep learning models for scientific applications (e.g., materials discovery, chemical modeling, process optimization) - Translate complex scientific and business problems into tractable AI/ML frameworks - Work with real-world structured and unstructured scientific data (e.g., experimental, simulation, and literature data) - Build and maintain data pipelines, feature engineering workflows, and model evaluation frameworks - Collaborate cross-functionally with scientists, engineers, and product managers to deliver production-ready solutions - Partner closely with Customer Success and client-facing teams to understand customer needs, translate requirements into AI/ML solutions, and support the successful deployment, adoption, and ongoing optimization of models in customer environments - Apply techniques such as supervised/unsupervised learning, generative models, and optimization algorithms - Contribute to the integration of models into scalable software platforms and APIs - Stay current with advancements in AI/ML and relevant scientific domains; evaluate and apply new methods where appropriate - Perform strategic research oriented towards improving NobleAI’s core technology - Communicate findings and model outputs clearly to both technical and non-technical stakeholders - Periodic travel to customer sites and attend industry events Qualifications - Ph.D. in Geoscience, Geophysics, Production Engineering, Reservoir Engineering, or other Energy-related quantitative discipline - Degree or coursework in Machine Learning - Hands-on experience applying machine learning to real-world problems in science and engineering - Strong background in machine learning: classical and deep learning techniques (examples may include CNNs, transformers, or embedding techniques, etc.), focus on supervised learning - Strong experience in Python and associated ML frameworks (Pytorch, Tensorflow, Keras, sklearn, etc.) - Demonstrated ability to effectively communicate complex technical details at a high level - Solid understanding of statistical modeling, optimization, and algorithm design - Proven ability to deploy models into production environments Preferred - Experience in either Natural Language Processing, Computer Vision, uncertainty quantification, or unsupervised learning approaches - Experience with cloud or distributed training frameworks (Azure, AWS, GCP) and MLOps practices - Experience in scientific domains such as chemistry, materials science, or energy - Familiarity with physics-informed ML, geological/reservoir modeling, and/or production forecasting and flow assurance optimization - Experience with generative AI methods (e.g., diffusion models, transformers) applied to scientific problems Benefits - Top-tier health benefits coverage, including medical, dental, vision, disability and life insurance - Flexible Paid Time Off & generous Holidays - Remote-first with optional co-working access at The Ion for our Houston-based employees - 401(k) with employer match - Equity package - Base Salary Range $190k - $220k (Depending on experience & Geographic location) - Performance-based bonus plan