Global Head of Solutions Engineering

Dust is hiring a Global Head of Solutions Engineering to build the technical customer-facing organization required for our next stage of growth. You will lead our global Solutions Engineering function across pre-sales and post-sales. This is a founding player-coach role. You will have authority over the organization's structure, hiring, performance, career paths, technical standards, coverage, resource allocation, and operating model. As a member of Dust's GTM leadership group, you will work closely with Sales, Customer Success, AI Deployment, Product, and Engineering. The opportunity is larger than building a traditional SE team. Location: New York. Office-first culture in Paris, London, San Francisco, and New York, with some flexibility to work from home.

Responsibilities

Define the global vision, strategy, and operating model for Solutions Engineering; Lead the organization across pre-sales and post-sales Solutions Engineering; Design the team structure, leadership model, roles, career paths, coverage model, operating standards, and performance systems; Significantly scale the team across Paris, New York, San Francisco and London; Hire, develop, and manage Solutions Engineers and future SE leaders; Establish a high talent bar; Create clear decision rights and operating rhythms across regions and functions; Allocate Solutions Engineering resources based on opportunity complexity, customer impact, strategic importance, and likelihood of success; Build systems and leadership capacity for the function to scale without linear headcount growth; Define how Dust qualifies, scopes, and executes complex enterprise technical evaluations; Partner with Sales leadership to improve technical win rate, evaluation conversion, pipeline coverage, and evaluation velocity; Develop the technical strategy for the most important enterprise opportunities; Analyze why Dust wins and loses technical evaluations and turn insights into improvements; Ensure successful evaluations transition into deployment with clear use cases, architecture, dependencies, ownership, and success criteria; Raise performance across discovery, demonstrations, solution design, pilots, architecture reviews, security validation, and technical handoffs; Build the post-sales Solutions Engineering motion for advanced, sticky use cases; Partner with Customer Success on strategic workshops, complex integrations, new architectures, and expansion; Establish a clear interface with AI Deployment; Turn successful engagements into reusable playbooks, reference architectures, technical assets, and AI-native workflows; Establish consistent methods for allocating resources, identifying risk, inspecting opportunities, and learning from outcomes; Turn recurring customer needs into input for Product and Engineering; Strengthen Dust's technical positioning across enterprise AI, integrations, security, governance, and agent architecture

Requirements

Built, scaled, or significantly transformed a Solutions Engineering, Solutions Architecture, Customer Engineering, Technical Account Management, or comparable customer-facing technical function; Recruited, retained, and developed customer-facing technical talent across multiple regions, segments, or customer motions; Owned measurable GTM outcomes and personally helped win complex enterprise opportunities; Understands both pre-sales technical execution and post-sales Solutions Engineering for adoption, value, and expansion; Combines technical credibility across enterprise architecture, integrations, security, governance, and AI systems with business judgment; Can stay close to strategic customers while building an organization that does not depend on them for every decision; Introduces the structure needed to scale without unnecessary process; Hands-on, low-ego leader who communicates clearly across Sales, Customer Success, AI Deployment, Product, and Engineering; Advises customers on where AI agents can create meaningful value; Reasons about model selection, prompting, context management, tool use, retrieval, evaluations, reliability, latency, and cost; Explains capabilities and limitations of AI systems to technical teams, business leaders, and executives; Identifies integration, security, data governance, and scalability risks; Reasons about APIs, authentication, data connectors, enterprise systems, and cloud architectures; Designs evaluation approaches that connect AI-system performance to customer and business outcomes; Coaches Solutions Engineers through difficult AI, architecture, and customer decisions; Recognizes excellent technical work and holds the organization to a high bar

Benefits

Competitive compensation: $275K–$350K OTE (NY); 130-160k EUR OTE (Paris); Significant equity package in a Sequoia-backed startup; Relocation support; Health insurance for you and your dependents; New MacBook Pro or Linux machine, monitor, keyboard, etc.; Beautiful offices in the heart of NYC and Paris; Opportunity to travel to offices and work with the global team multiple times a year; Regular team events and offsite