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The Sales Engineer will serve as a forward-deployed technical expert, independently leading proof-of-value engagements with enterprise prospects. This role combines deep technical expertise in data modeling and ontology design with hands-on implementation skills to deliver rapid-cycle pilots that demonstrate Shelfs value and convert to closed deals.
This is a unique opportunity to work at the cutting edge of AI and data quality, designing custom data models and configuring reasoning agents to solve complex enterprise challenges. Youll operate with significant autonomy, embedded with prospects during 3-5 day pilots, iterating quickly to prove measurable value. Your ability to understand customer data architectures, design semantic models, and deliver technical solutions will be crucial to our sales success.
Reporting to the Field CTO, youll partner with Account Executives throughout the sales cycle, with primary responsibility during technical evaluation and POV phases. Beyond new customer acquisition, youll support expansion opportunities within existing accounts, contributing to our land-and-expand growth strategy. Youll work directly alongside Engineering during POVs and collaborate on building reusable templates and best practices.
The ideal candidate brings 5-10 years of experience combining technical depth in data platforms and modeling with customer-facing skills. As a self-starter, youll quickly ramp through hands-on internal projects that mirror customer engagements. Youre comfortable writing scripts, leveraging AI coding tools like Claude Code, and delivering approximately 8-10 POVs per quarter. Experience with knowledge graphs, ontologies, or semantic technologies is strongly preferred, though exceptional data modeling expertise can substitute.
There is no AI Strategy without a Data Strategy. Getting GenAI to work is mission critical for most companies but 90% of AI projects havent deployed. Why? Poor data quality - it is the #1 obstacle companies have in getting GenAI projects into production.
Weve helped some of the best brands like Amazon, Mayo Clinic, AmFam, and Nespresso solve their data issues and deploy their AI strategy with Day 1 ROI.
Simply put, Shelf unlocks AI readiness. We provide the core infrastructure that enables GenAI to be deployed at scale. We help companies deliver more accurate GenAI answers by eliminating bad data in documents and files before they go into an LLM and create bad answers.
Shelf is partnered with Microsoft, Salesforce, Snowflake, Databricks, OpenAI and other big tech players who are bringing GenAI to the enterprise.
Our mission is to empower humanity with better answers everywhere.
Success Metrics To be successful in this role, you should aim to achieve the following within the first 18 months:
The Sales Engineer will serve as a forward-deployed technical expert, independently leading proof-of-value engagements with enterprise prospects. This role combines deep technical expertise in data modeling and ontology design with hands-on implementation skills to deliver rapid-cycle pilots that demonstrate Shelfs value and convert to closed deals.
This is a unique opportunity to work at the cutting edge of AI and data quality, designing custom data models and configuring reasoning agents to solve complex enterprise challenges. Youll operate with significant autonomy, embedded with prospects during 3-5 day pilots, iterating quickly to prove measurable value. Your ability to understand customer data architectures, design semantic models, and deliver technical solutions will be crucial to our sales success.
Reporting to the Field CTO, youll partner with Account Executives throughout the sales cycle, with primary responsibility during technical evaluation and POV phases. Beyond new customer acquisition, youll support expansion opportunities within existing accounts, contributing to our land-and-expand growth strategy. Youll work directly alongside Engineering during POVs and collaborate on building reusable templates and best practices.
The ideal candidate brings 5-10 years of experience combining technical depth in data platforms and modeling with customer-facing skills. As a self-starter, youll quickly ramp through hands-on internal projects that mirror customer engagements. Youre comfortable writing scripts, leveraging AI coding tools like Claude Code, and delivering approximately 8-10 POVs per quarter. Experience with knowledge graphs, ontologies, or semantic technologies is strongly preferred, though exceptional data modeling expertise can substitute.
There is no AI Strategy without a Data Strategy. Getting GenAI to work is mission critical for most companies but 90% of AI projects havent deployed. Why? Poor data quality - it is the #1 obstacle companies have in getting GenAI projects into production.
Weve helped some of the best brands like Amazon, Mayo Clinic, AmFam, and Nespresso solve their data issues and deploy their AI strategy with Day 1 ROI.
Simply put, Shelf unlocks AI readiness. We provide the core infrastructure that enables GenAI to be deployed at scale. We help companies deliver more accurate GenAI answers by eliminating bad data in documents and files before they go into an LLM and create bad answers.
Shelf is partnered with Microsoft, Salesforce, Snowflake, Databricks, OpenAI and other big tech players who are bringing GenAI to the enterprise.
Our mission is to empower humanity with better answers everywhere.
,[Lead independent POV engagements with enterprise prospects, designing custom ontologies and configuring solutions within 3-5 day cycles, Conduct technical discovery and present solutions to Data Engineers, Enterprise Architects, and AI/ML Engineers, Support Account Executives throughout sales cycles with primary ownership during technical evaluation and POV phases, Work directly with Engineering on technical challenges and custom implementations during POVs, Continue engagement through deal closing and initial customer onboarding alongside Customer Success, Support existing customer expansion opportunities as part of land-and-expand strategy, Collaborate with Field CTO to build reusable ontology templates and POV best practices Requirements: Data engineering, Python, Data pipelines, Cloud, AWS, Azure, GCP, Data science, Claude Code, Cursor, GitHub Copilot, B2B SaaS, AI, ML Additionally: Stock options, GitHub Copilot subscription, LLM credits.