• Create and optimize content engineered to surface in LLM responses — question-led pages, comparison pages, etc. • Track where the brand appears (and doesn't) across major AI surfaces and report on coverage trends in specific products (video). • Identify high-intent prompts we should be visible for, and ship the supporting content. • Ship two pieces of work per day on average — content, live community insertions, or successful listicle outreach. • Outreach for inclusion in industry listicles, review sites, and category roundups. • Manage and grow our presence on G2, Capterra, TrustRadius, and similar platforms in collaboration with the internal team. • Build relationships with analyst-style writers and journalists publishing in the category. • Participate authentically in Reddit, Quora, Hacker News, and other community discussions where the brand should be in the conversation. • Identify high-traffic threads where competitors are mentioned and we're not — and change that. • Build a presence that's helpful first and promotional second. (Communities will sniff out anything else.) • Log every experiment in the team tracker. • Monitor AI visibility weekly across the tools we use to measure it. • Write short async recaps on what moved coverage, what didn't, and why.

• Embed in each function before building anything — learn how work actually moves, surface the workflows worth automating, and build them using the platform stack the team is assembling. Production adoption is the metric, not a polished demo. • Configure and deploy the End User Workbench for each function you support — tailored to how that team actually works, not a generic starting point. • Scope, build, and ship agentic workflows in production across business functions like legal review, financial operations, marketing, HR, and revenue. These are not prototypes. • Evaluate AI-native vendor solutions alongside the functions you support — separating genuine capability from hype and making honest build-versus-buy recommendations. When something real exists, recommend it. When it doesn't, build it. • Work alongside people on real problems until they can scope, build, and own their own agent workflows. The measure is when teams you support are shipping without you. • Support your partners through agent ownership — what it means to maintain a workflow when you've never operated software before. You'll work through those questions in practice, not in theory. • Return field signal to the platform. What you learn forward-deployed shapes how the End User Workbench evolves — and in some cases you will build the solution directly into it. • Partner with Legal, Privacy, and Security to keep data boundaries built into every workflow from the start — not appended at the end. • Document what works — configuration templates, workflow patterns, and deployment approaches that make the next engagement faster and safer.

• Oversee the performance and reliability of 1NCE’s automated first-line customer support platform. • Monitor the daily performance of the automated first-line AI support system. • Review operational dashboards, workflow execution logs, and ticket flows to ensure system stability and performance. • Identify workflow interruptions, failed automation steps, or abnormal behavior within the AI support system. • Ensure SLA adherence for automated support processes. • Manage operational incidents affecting automated support workflows. • Identify and resolve basic operational issues within AI-driven workflows. • Escalate technical problems to engineering or platform teams when required. • Ensure rapid recovery of automated support processes during incidents. • Manage complex or exceptional customer cases that cannot be resolved by automated workflows. • Monitor AI support response quality and resolution performance. • Identify improvement opportunities in automation workflows and operational processes. • Coordinate with internal teams such as Customer Support, Product, Platform Engineering, and AI operations teams.

• Design and solve challenging Physics problems to probe the limitations of large language models. • Create clear, high-quality, step-by-step solutions with well-articulated reasoning. • Collaborate with LLM researchers to align problems with evaluation goals, especially in areas where models struggle (e.g., abstraction, multi-step reasoning, symbolic manipulation). • Help define new evaluation benchmarks based on Physics curricula spanning early undergraduate to PhD-level topics.