Solutions Engineer

The AI platform for investors and bankers that generates alpha and drives upside.
Founded in 2020 by George Sivulka and backed by Peter Thiel and Andreessen Horowitz, Hebbia powers investment decisions for Black Rock , KKR , Carlyle , Centerview , and 40% of the world’s largest asset managers. Our flagship product, Matrix, delivers industry-leading accuracy, speed, and transparency in AI-driven analysis. It is trusted to help manage over $30 trillion in assets globally.
We deliver the intelligence that gives finance professionals a definitive edge. Our AI uncovers signals no human could see, surfaces hidden opportunities, and accelerates decisions with unmatched speed and conviction. We do not just streamline workflows. We transform how capital is deployed, how risk is managed, and how value is created across markets.
Hebbia is not a tool. Hebbia is the competitive advantage that drives performance, alpha, and market leadership.
About the Solutions Engineering Team

The Solutions Engineering team converts buyer interest into enterprise conviction. In complex evaluations where customers are underwriting risk, they structure ambiguous problems, build tailored AI workflows, and validate value inside real data environments. This work directly drives win rates, accelerates deal cycles, and gives executives confidence to adopt — making the team a primary driver of revenue and product credibility, not a sales support function.

As Hebbia moves up-market and into new segments and geographies, every expansion requires deeper technical validation, requiring the Solutions Engineering team to rapidly grow and scale. Their learnings compound into repeatable solution patterns that sharpen positioning and unlock larger deals, turning the function into a core lever of scalable growth.

As a Solutions Engineer at Hebbia
, you are a revenue-critical leader who owns the technical strategy in complex, high-scrutiny enterprise evaluations. You operate at the intersection of commercial leadership and AI execution, shaping structured solution strategies while proving real value during the buying process.

You bring clarity to ambiguity. You translate complex enterprise problems into structured, Hebbia-driven solution approaches that executives can confidently act on. At the same time, you design tailored AI-powered workflows and evaluations that validate impact, reduce decision risk, and strengthen the commercial narrative.

You operate as a trusted advisor to sophisticated buyers navigating ambiguity, risk, and scale. Your job is to clarify thinking, reduce decision risk, and prove value in environments where accuracy, speed, and explainability matter.

This role is based out of Hebbia’s New York City office in SoHo.

• Own technical strategy from discovery through close in complex enterprise sales cycles
• Lead rigorous technical discovery to understand workflows, data realities, constraints and executive success criteria
• Translate ambiguous customer challenges into clear structured Hebbia solution frameworks
• Design and configure tailored AI-powered workflows to prove differentiated value during evaluations
• Deliver high-stakes live demos and technical discussions with senior stakeholders
• Surface risks early, challenge assumptions, and shape mitigation strategies that accelerate deal progression
• Partner closely with Sales to increase win rates, deal velocity, and executive confidence
• Provide structured product feedback that sharpens positioning and informs roadmap decisions

• 5+ years in a customer-facing technical role within complex enterprise sales environments (Solutions Engineering, Sales Engineering, Consulting, Implementation, or similar)
• Deep experience with AI systems, data platforms, LLM based workflows or decision intelligence platforms
• Demonstrable executive presence, able to earn trust from C-Suite practitioners
• Strong at structuring ambiguous problems into clear, defensible solution approaches
• Strong preference for experience selling to or performing Analyst roles in Banking, Asset Management, Private Markets, Legal or Enterprise Knowledge teams in complex enterprise environments

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