Sobre esta vaga de Co-Founder-Strata na AI Fund
Strata is on a mission to turn static research and one-off inquiries into living, goal-oriented projects that stay current, preserve institutional knowledge, and help teams make better decisions.
We’re building an intelligence and operations platform that helps teams continuously monitor and interpret external developments in the context of their company’s strategy, obligations, risks, and goals. Today, enterprise work is fragmented across docket sites, filings, Word documents, spreadsheets, shared drives, emails, and expert memory. The costly problem is not simply knowing that something changed. It is understanding what changed, whether it is draft or final, how it affects the business, which projects or documents need to be updated, who needs to review the impact, and what action should follow.
Strata turns enterprise work from static research and one-off documents into living, goal-oriented projects that stay current, preserve institutional knowledge, and help teams make measurable progress over time.
This is a rare opportunity to lead a venture-backed company at inception, with the benefit of validated customer demand, a clear initial wedge in utilities and energy, and a compelling path toward becoming the trusted operating system for regulated decision-making. Strata is poised to build toward product-market fit in a large, high-stakes market where regulatory change directly affects economics, compliance, capital planning, and strategic outcomes.
We are looking for a Co-Founder with experience building B2B SaaS. The ideal candidate will have a deep appreciation for AI’s ability to augment expert knowledge work, and the founder-level drive to turn a sharp market insight into a category-defining company. There is an existing design partner/enterprise customer already in place, AES Corporation. AES is a Fortune 500 global energy company that is accelerating the future of energy.
You’ll be working alongside founding partners from AI Fund and AES to build Strata from the ground up. This role will be based in the San Francisco Bay Area and relocation will not be considered. This role is co-founding a pre-seed start-up and will be equity heavy in addition to the base salary.
Responsibilities Include:
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Define and drive the company vision for a new category of regulatory intelligence and operations software for regulated enterprises.
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Work closely with early customers to deeply understand regulatory workflows, pain points, buying dynamics, and the highest-value use cases.
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Translate customer needs into a focused product roadmap for an intelligent regulatory workspace that supports research, change monitoring, precedent analysis, impact assessment, filing preparation, and strategy updates.
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Provide technical leadership to build domain-aware AI agents that can search, read, compare, cite, and interpret regulatory materials with the level of trust required for high-stakes enterprise work.
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Recruit and lead a world-class team across go-to-market, product, engineering, and customer success.
Must Haves For The Role Include:
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Demonstrated experience at an early-stage B2B SaaS company, ideally from 0 to 1 and into early scale.
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Experience working with AI, data-centric products, workflow software, enterprise knowledge systems, or vertical SaaS.
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Hands-on experience building with LLMs, particularly agentic architectures, tool use, and function calling.
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Ability to translate a complex customer problem into a clear product vision.
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High empathy for expert users doing high-stakes work, especially teams whose decisions depend on accuracy, auditability, citations, and institutional knowledge.
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Ability to move fast with clarity, judgment, and conviction.
Nice To Haves Include:
- Experience building or commercializing AI products involving retrieval, knowledge management, document intelligence, workflow automation, or expert-in-the-loop review.
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Experience building products that require trust, traceability, citations, auditability, and high-quality human review.
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Prior exposure to enterprise data partnerships, public records, regulatory data, or domain-specific knowledge systems.
