Salary Range: 110000 to 212000 (Currency: USD) (Pay period: per-year-salary)

Responsibilities

• Evangelize TiDB’s technical advantages for intelligent platforms and applications. Identify, connect with, and convert prospects who can benefit from TiDB’s multi-model, cost-effective, scalable data architecture.
• Develop strategies to source and engage potential AI-centric customers and partners. Manage lead funnels, qualifying prospects, and orchestrating the sales cycle from introduction to proof-of-concept (PoC).
• Conduct presentations, demos, and workshops showcasing TiDB’s strengths for AI use cases (e.g., real-time data processing, agent memory, vector embeddings). Communicate complex technical concepts in a clear, compelling way.
• Collaborate with internal teams (Product, Engineering, Marketing) to refine value propositions tailored to AI workloads. Cultivate partnerships and alliances that broaden TiDB’s reach in the AI ecosystem.
• Serve as the primary point of contact from onboarding through to integration. Work closely with technical leaders and executives to ensure smooth deployment, monitor usage metrics, and drive continued growth.
• Track customer adoption rates and deployment metrics. Provide regular reports on customer feedback, usage trends, and emerging AI market opportunities.

Qualifications

• 5+ years in partnerships, business development, or sales targeting tech startups (AI/ML, infra, or devtools).
• A track record of building and managing customer pipelines, ideally in technical sales, solutions, or customer success capacity. Ability to align customer needs with product capabilities.
• Comfortably present to both executive stakeholders and hands-on engineers. Able to distill complex technical details into clear, value-driven messaging.
• Self-directed, with a knack for discovering new opportunities and forging paths in uncharted territories. Thrives in a fast-paced environment shaped by rapid AI and data innovation.

Good to Have

• Experience as a BDR/SDR in a venture-backed startup, preferably in the software infrastructure, developer tools, or data space.
• Experience with databases or data infrastructure. Familiarity with SaaS, AI workflows, such as ML model training or vector-based search.
• Established network within the AI community (e.g., user groups, open-source contributors, conference speakers, VC communities).
• Experience communicating with technical buyers (e.g., developers, engineers, CTOs) is a plus.