Senior Solutions Engineer - Tabnine

Description

A bit about the job

Interested in being part of the go-to-market team selling real-world Generative AI that is moving from pilot projects into production across large engineering organizations?

Tabnine is building out a senior pre-sales team that can understand complex customer environments and map those requirements to the right Tabnine solution. For this role, the conversation is no longer just about an IDE plugin helping individual developers be more productive. It is about the Enterprise Context Engine: a context layer that gives AI systems structured understanding of repositories, services, dependencies, APIs, documentation, and architectural relationships, and can improve the tools customers already use, including Tabnine , Cursor, GitHub Copilot, and Claude Code. If you have a software development / DevOps background and a proven track record in solution engineering, especially in front of enterprise engineering buyers, we want to hear from you.

This role covers the US region. You will partner with Sales to run technical discovery, demos, architecture conversations, POVs, and rollout planning with engineering leadership, platform teams, architecture, DevEx, security, and the developers who will use the product day to day.

A bit about us

Tabnine helped pioneer AI-assisted software development and today provides both an AI Coding Platform and a standalone Enterprise Context Engine. The Tabnine Context Engine is the missing layer that makes AI more reliable, safe, and effective in the enterprise. Tabnine can be deployed as SaaS, in a private VPC, on-premises, or fully air-gapped, giving customers flexibility around privacy, security, and compliance. We’re looking for top-tier talent to join the Tabnine team and we’d love to hear from you.

Responsibilities

Responsibilities:

• Own the technical evaluation components of the sales process for accounts across your assigned territory.
• Partner closely with Account Executives to drive technical strategy from first meeting through close and expansion.
• Interface with the technical decision makers or influencers in key accounts: VP Engineering, CTO, platform engineering, architecture, DevEx, AppSec, and developers.
• Understand how customers build software today: their architecture, SDLC, developer workflows, governance requirements, and what would need to be true for AI to be trusted in their environment.
• Articulate the various Tabnine deployment options: SaaS, VPC (AWS, GCP, Azure), on-prem, and fully air-gapped deployments.
• Run live demos and POVs that show how the Enterprise Context Engine helps AI tools understand system boundaries, dependencies, architectural patterns, code review signals, and organizational memory, not just the file in front of them.
• Define and execute a scalable POV program with