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The Account Executive will leverage technical expertise in enterprise SaaS sales to drive revenue growth, build partnerships, and represent Neural Concept's technology in engineering sectors.

The role involves engaging with customers, translating insights into product requirements, and collaborating across departments. The candidate should have engineering experience and strong product instincts to manage a product discovery process.

Own revenue forecasting, GTM analytics, RevOps tech stack, and sales process design. Partner with Finance and Sales to build commercial infrastructure, drive forecasting accuracy, automate workflows, and deliver executive-ready insights to improve pipeline velocity and revenue outcomes.

The Consumer Electronics Solution Owner will lead the development of engineering workflows for the consumer electronics industry, partnering with customers to define solutions and ensure value delivery.

Lead technical pre-sales on strategic accounts across DACH & Nordics: run discovery, define AI transformation roadmaps, deliver client-tailored demos and Statements of Work, grow the regional SE team, and partner with Sales, FDE, Product and R&D to close and scale multi-year engagements.

As a Technical Account Manager, you'll drive customer adoption of AI tools, manage projects, deliver demos, and advocate for user feedback while collaborating across teams to ensure customer success.

Drive pre-sales technical engagement: discovery, tailored demos, define AI strategies and SOWs, run steering committees, enable user adoption, identify upsell opportunities, and bridge Sales, Forward Deployed Engineering, Product and R&D.

The Technical Account Manager will drive customer success, manage accounts, deliver onboarding and demos, and align engineering solutions with customer needs.

Own end-to-end product lifecycle for a vertical engineering solution: run customer discovery, define and prioritize requirements, prototype with AI-assisted tools (including scripting), lead cross-functional CAD/CAE/ML engineering teams to production, and track adoption and value delivery to inform the global roadmap.

The ML Application Engineer will work with customers to apply machine and deep learning to engineering challenges, develop solutions using Python, and train customer teams in AI adoption.