• Design, implement, and tune inference pipelines for large language models and other AI workloads, targeting maximum throughput and minimum latency. • Apply state-of-the-art optimization techniques: quantization (INT4/INT8/FP8), model pruning, speculative decoding, continuous batching, and kernel fusion. • Optimize inference-serving stacks, including vLLM, TensorRT-LLM, ONNX Runtime, and similar frameworks, for production deployment on CIQ’s OS platform. • Profile and tune GPU/accelerator utilization across the full inference stack, from model weights and memory bandwidth to CUDA kernels and driver overhead. • Establish inference performance baselines and regression detection across CIQ’s AI-focused solutions. • Design and optimize distributed training pipelines for large-scale models, including data, model, tensor, and pipeline parallelism strategies. • Tune training efficiency through mixed-precision training, gradient checkpointing, activation recomputation, and optimizer-level improvements. • Benchmark training throughput and scaling efficiency across multi-GPU and multi-node configurations on CIQ’s infrastructure. • Collaborate with infrastructure and performance teams to resolve training bottlenecks at the network (RDMA/InfiniBand), storage, and OS layers. • Stay current on frontier model architectures and training techniques, including MoE models, RLHF pipelines, and emerging post-training methods. • Build and maintain a library of turn-key AI workload examples that run on CIQ’s platform, covering inference serving, fine-tuning, batch processing, RAG pipelines, and agentic workflows. • Develop both internal reference pipelines for CI/testing and customer-facing examples designed for immediate productivity on CIQ’s OS and Fuzzball. • Package workloads using containers to deliver portable, reproducible AI environments across HPC and cloud-native settings. • Create compelling, well-documented demos and reference architectures that communicate CIQ’s AI capabilities to technical and business audiences alike. • Partner with product and customer success teams to translate real-world AI use cases into reusable, production-quality examples. • Build and maintain AI-powered engineering tooling - leveraging LLM-based agents, automated analysis pipelines, and AI-assisted code generation to accelerate the broader engineering organization. • Champion an AI-first development culture: identify opportunities where AI tooling can reduce toil, surface insights faster, and improve software quality across CIQ’s products. • Evaluate and integrate emerging AI frameworks, libraries, and hardware as they become relevant to CIQ’s customers and product roadmap. • Contribute to open-source AI tooling and frameworks where relevant, reinforcing CIQ’s technical reputation in the community. • Develop deep expertise in CIQ’s Fuzzball platform, its architecture, scheduling model, and workload execution environment. • Integrate AI training, inference, and pipeline workloads into Fuzzball-based CI/CD and production pipelines. • Contribute to Fuzzball’s AI workload story: ensure the platform is a first-class environment for running AI workloads efficiently and at scale. • Help characterize and improve Fuzzball’s performance for AI-specific access patterns and resource demands. • Develop broad familiarity with the full CIQ product portfolio, including Rocky Linux and RLC (and its variants), Fuzzball, Apptainer, and Warewulf, and understand how AI workloads interact with each layer. • Collaborate closely with the Performance Engineering team to ensure AI workloads benefit from and contribute to CIQ’s systems-level optimization work.

• 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.