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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.

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.

Build and ship multimodal AI detection systems. Train large-scale deep learning models to detect AI-generated images and own full lifecycle: data pipelines, training, evaluation, deployment, monitoring, and retraining. Operate production inference and batch systems, design monitoring for drift, and collaborate to translate models into product features and publications.

Own technical relationships with enterprise and education customers: scope, prototype, integrate, deploy, and troubleshoot full-stack integrations (APIs, SSO, LMS, data pipelines). Run demos/POCs, handle security reviews, and produce integration guides and runbooks while feeding product insights back to engineering.

Seeking a Senior Software Engineer to design and scale backend systems, focusing on reliability and performance for AI detection platforms using Django and AWS.

As an AI Research Scientist, you'll train deep learning models to detect AI-generated content, conduct experiments, and contribute to product development through research and publications.

The Machine Learning Engineer will build data pipelines, manage multi-GPU training infrastructure, optimize code, and deploy inference pipelines for LLMs.

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.