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Own Pangram’s design system, brand, and visual identity across products, web, and marketing. Design and ship website pages, create marketing assets, contribute to UI/UX and onboarding, conduct lightweight user studies, and manage visual quality assurance. The role partners closely with founders, design, and product leadership while helping shape the company’s emerging design function.

Own inbound enterprise sales from qualification through close: conduct discovery and demos, manage pilots, develop proposals, negotiate and close deals, maintain pipeline and forecasts, partner with Customer Success, and provide market feedback to leadership.

Own the full sales cycle for educational institutions: qualify leads, run discovery and demos, coordinate pilots, build stakeholder relationships, negotiate contracts, maintain pipeline, and partner with Customer Success to implement solutions.

First dedicated Customer Success Manager responsible for onboarding, retention, and expansion of B2B accounts. Build and execute success playbooks, monitor customer health and KPIs, run training and business reviews, handle support and compliance requests, identify upsell opportunities, deliver reports/insights, and advocate customer needs to product and internal teams.

Design and maintain large-scale data pipelines to generate synthetic text, manage distributed multi-GPU LLM training infrastructure, profile and optimize training and inference code, and deploy efficient inference pipelines for serving LLMs in production. Collaborate across research and engineering and contribute to publishing research and innovations.

Lead and build the paid acquisition function: run Google Ads end-to-end, launch and test paid social channels (Meta, LinkedIn, TikTok), brief and iterate on creative/copy/landing pages, run weekly experiments, and measure conversions while optimizing CAC, payback, and LTV for consumer and B2B audiences.

Produce and maintain technical and user-facing content about Pangram's AI detection models. Author and edit research papers, technical reports, model cards, benchmarks, and guides. Translate model architecture, training methodology, and statistical evaluation into accurate, accessible explanations for technical and non-technical audiences. Collaborate with research and engineering to ensure documentation accuracy.