• Lead the design, evaluation, and deployment of our production photogrammetry pipeline — feature detection and matching, structure from motion, multi-view stereo, monocular depth estimation, mesh reconstruction, and the supporting infrastructure that makes them work at scale. • Drive integration and evaluation of newer 3D reconstruction approaches — gaussian splats, foundation 3D models (MASt3R / DUSt3R-class and successors), and what comes next — where they earn their place in the pipeline. • Optimize 3D systems for speed, accuracy, and robustness at production scale. • Use the right tool for the problem — classical 3D computer vision when it wins, learned approaches when they win. • Own the hardest technical investigations end-to-end — initial triage through production rollout and long-tail support. • Set the technical bar for photogrammetry on the team — review designs, raise the quality of experiments, codify what "great" looks like. • Own the production observability standard for photogrammetry — KPIs, instrumentation (metrics, logs, dashboards, alerts), and the processes that catch data-quality issues early and sustain performance at scale. • Mentor engineers on the team, then take on 1–2 direct reports in your first year and grow your scope over time as the sub-team expands. • Drive execution of the photogrammetry core roadmap in tight partnership with CV leadership. Translate strategic direction into shipped work, sequence it well, and call out when something isn't going to land in time to course-correct. • Codify debugging and investigation playbooks into reusable skills to empower the team and support the organization, minimizing escalations. • Reshape how the team uses AI across the SDLC — driving adoption of AI-first and prototype-first workflows and pushing the bar on what these tools should do for us. • Author reusable tooling that scales the team — agent skills, prompts, debugging playbooks, custom tooling — so other engineers (and our support org) can stand on your shoulders rather than escalate. • Run rigorous evaluations of AI tools — where they help, where they hurt, and how to tell. • Drive the adoption of prototype-first and AI-first development workflows. • Review agent-generated code with the same rigor as human-written code, and establish standards for trust and reliability. • Treat the AI tooling landscape as a moving target: actively track what's new, run it against current problems, and champion effective new tools across the team.

• Architect, design, and implement scalable web applications and internal tools to support Marketing Operations and eCommerce workflows • Serve as a technical lead on projects, breaking down complex problems and guiding implementation across teams • Collaborate closely with product managers, designers, and business stakeholders to align technical solutions with strategic objectives • Conduct technical planning, estimation, and execution of medium to large-scale projects • Write clean, maintainable code with strong documentation and automated tests • Establish and enforce engineering best practices across the development lifecycle • Perform peer code reviews and provide mentorship to software engineers • Define and implement QA strategies, including unit tests, integration tests, regression testing, and monitoring protocols • Troubleshoot and resolve complex bugs and system issues in production and staging environments • Drive improvements in system reliability, performance, security, and scalability • Contribute to the evolution of the Workbench CMS and other internal platforms used for content creation, localization, and campaign execution • Promote a culture of continuous learning, knowledge sharing, and technical excellence

• Lead the development of scalable and robust internal tools empowering the Marketing and eCommerce teams • Deliver high-quality technical solutions and mentor junior developers • Drive architectural decisions aligning with long-term product and business goals • Architect, design, and implement scalable web applications and internal tools to support workflows • Collaborate closely with product managers, designers, and business stakeholders • Write clean, maintainable code with strong documentation and automated tests • Establish and enforce engineering best practices across the development lifecycle • Perform peer code reviews and provide mentorship • Define and implement QA strategies • Troubleshoot and resolve complex bugs and system issues in production and staging environments • Drive improvements in system reliability, performance, security, and scalability • Contribute to the evolution of internal platforms used for content creation

• Identify business opportunities that can be addressed through data and AI products • Work closely with Sales, CRM, Operations, Product, and Engineering • Lead discovery and hypothesis validation for new data products • Design and develop AI-based products • Build conversational solutions for data exploration • Develop agents capable of monitoring performance, detecting deviations, and executing corrective actions • Define success metrics and track the value generated by developed products • Ensure quality, observability, reliability, and governance of data products • Serve as a technical reference for the team on Data Products and Artificial Intelligence topics • Rapidly prototype new solutions and run business experiments • Connect the company's strategic objectives with practical AI application opportunities