Jobs at Vertex, Inc.

Leads strategy, execution, and the full lifecycle for tax technology products, from ideation through launch and optimization. Conducts market research, aligns cross-functional teams, drives roadmap and innovation decisions, and identifies integration opportunities across product lines. Uses customer, market, and performance data to guide direction, mentors team members, represents Vertex at industry events, and maintains awareness of competitors. Requires extensive product management experience, SaaS and cloud expertise, strong business acumen, and occasional travel.

Leads the discovery, development, launch, and scaling of AI-first product capabilities. Translates ambiguous customer problems into hypotheses, requirements, evaluation criteria, roadmaps, and measurable outcomes. Partners with engineering, UX, tax SMEs, go-to-market, and customer success to prototype and operationalize reliable, compliant AI solutions. Owns AI quality, monitoring, performance, feedback loops, responsible AI practices, and continuous improvement while influencing cross-functional stakeholders and mentoring junior product owners.

Own the full enterprise sales cycle across the Midwest, from prospecting and territory planning through negotiation and close. Build executive relationships, manage complex multi-stakeholder deals, align tax technology and enterprise software solutions to customer outcomes, and exceed revenue quotas. Use AI-enabled tools and data to improve pipeline development, forecasting, and sales productivity while collaborating with marketing, consulting, sales development, and customer success teams.

Delivers GTM commercial analysis, dashboards, reporting, forecasting, workflow automation, and AI-assisted solutions. Partners with commercial, Finance, Data & Analytics, IT, and regional teams to improve decision-making, standardize metrics, validate outputs, resolve data issues, and reduce manual work. Owns assigned initiatives, documents business logic, supports testing and adoption, and develops reusable capabilities across regions.

Lead enterprise model-training strategy for Commercial AI products: fine-tune LLMs (QLoRA/LoRA/PEFT), train traditional ML models, design large-scale data pipelines, enforce data governance and PII handling, build reproducible experiment/training pipelines, optimize GPU/distributed training costs, and mentor teams to operationalize models into production.

Lead design and implementation of the enterprise AI orchestration and retrieval platform: build MCP servers, define tool-surface and multi-agent patterns, design RAG systems and chunking, implement routing/context/window management and observability, set evaluation and safety standards, onboard product teams, and mentor engineers to raise orchestration and retrieval maturity.