• Develop and evaluate a real-world AEC design use case, supporting design decisions through a collaborative, multi-agent AI workflow • Iteratively prototype, test, and refine AI-driven workflows, documenting both successes and failures • Build and maintain code repositories, along with clear documentation and compelling visual outputs • Identify and define new use cases and assess industry applicability of developed solutions • Communicate research outcomes through internal presentations at the conclusion of the internship • Contribute to or lead the preparation of a peer-reviewed publication for an external AI and/or AEC conference

• Experience and research AI workloads and DL models specifically tailored for large-scale deep learning LLM training on NVIDIA supercomputers with a focus on High-performance networking. • Benchmarking, Profiling, and Analyzing the performance to find bottlenecks and identify areas of improvement and optimizations, with a strong emphasis on networking aspects. • Implement performance analysis tools. • Collaborating with many teams from HW to SW to provide performance analysis insights. • Define performance test planning, set performance expectations for new technologies and solutions, and work to reach the performance targets limits.

• Monitor daily dashboards and alerts to ensure AI workflows complete successfully • Identify, restart, and troubleshoot failed or stuck processes in real time • Perform light debugging and escalate complex issues to the Product or Engineering teams • Maintain consistency in uptime, reliability, and data flow across systems • Track and analyze key performance metrics to identify recurring errors or inefficiencies • Collaborate with Product and Engineering to refine and improve AI model behavior • Annotate or validate data outputs to ensure AI accuracy and consistency • Contribute insights that drive measurable improvement in system reliability • Use tools like **Extend**, **Basepilot**, and internal platforms to manage AI operations • Test and evaluate new frameworks and automation tools that could improve performance • Document repeatable troubleshooting playbooks and standardize operational workflows • Maintain logs of incidents, resolutions, and root causes for visibility and learning • Run light **SQL queries** and use **Postman or API clients** to verify data flows • Create or enhance dashboards and reports that surface system health and performance

• Drive measurable improvements in key customer and business metrics (e.g. conversion, activation, retention, task success) through design-led experimentation. • Run design experiments, partnering with data, product and engineering to define hypotheses, success metrics, and evaluation frameworks. • Design and iterate on customer-facing experiences across Kraken’s trading, onboarding, and account surfaces based on experiment results and user insight. • Apply AI selectively and responsibly to improve personalization, decision support, and usability, with a clear focus on outcomes and user trust. • Own design quality and experiment performance, taking accountability for learnings, trade-offs, and results. Not just outputs. • Influence product strategy by translating experiment insights into clear recommendations and next steps. • Balance speed and rigor, enabling rapid experimentation while maintaining high standards of UX, accessibility, and brand consistency. • Contribute actively to design reviews and experiment readouts, sharing insights that raise the effectiveness of design and experimentation across teams. • Continuously identify new opportunities where experimentation and AI can unlock customer or business value.