About SandboxAQ

SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges. The company’s Large Quantitative Models (LQMs) power advances in life sciences, financial services, navigation, cybersecurity, and other sectors.

We are a global team that is tech-focused and includes experts in AI, chemistry, cybersecurity, physics, mathematics, medicine, engineering, and other specialties. The company emerged from Alphabet Inc. as an independent, growth capital-backed company in 2022, funded by leading investors and supported by a braintrust of industry leaders.

At SandboxAQ, we’ve cultivated an environment that encourages creativity, collaboration, and impact. By investing deeply in our people, we’re building a thriving, global workforce poised to tackle the world's epic challenges. Join us to advance your career in pursuit of an inspiring mission, in a community of like-minded people who value entrepreneurialism, ownership, and transformative impact.

The Opportunity

SandboxAQ’s AI Simulation (AISim) division is building Large Quantitative Models (LQMs)—AI grounded in physics, chemistry, and biology rather than language—to solve complex problems that traditional AI models cannot. Through AQBioSim and AQChemSim, the team helps leading R&D organizations in biopharma, chemicals, and advanced materials accelerate discovery and development across drug discovery, batteries and energy storage, catalysts, and advanced alloys.The AISim marketing team works with a highly technical, scientific audience, including leaders in computational and medicinal chemistry, drug discovery, and materials R&D. The team’s mission is to build credibility with these sophisticated buyers and translate that credibility into measurable demand and pipeline.

The AISim team is looking for a Growth Marketing Manager to join our team. This role is central to building a full-funnel growth engine from the ground up—from generating awareness and capturing demand to influencing and sourcing revenue. This person will own the strategy and execution of multi-channel demand generation, using data to determine what works, where to invest, and how to continuously improve performance. The role will focus on four core goals: building scalable demand generation programs, increasing sourced and influenced pipeline, establishing rigorous measurement and attribution, and creating a data-driven growth playbook for AISim.

Key Responsibilities

Own full-funnel demand generation: Design, launch, and optimize multi-channel campaigns across paid media, SEO, email and nurture, webinars, and emerging channels such as content syndication, with performance measured through sourced and influenced pipeline.

Build the growth engine from the ground up: Establish campaigns, workflows, reporting, and operating processes from a near-blank slate, prioritizing investments across messages, channels, and platforms based on performance data.

Execute programs hands-on: Plan and run programs end to end—including webinars, audience development, campaign execution, and performance analysis—while strategically using vendors to extend your reach.

Measure, analyze, and optimize performance: Develop and report on funnel metrics, channel efficiency, cost per pipeline dollar, campaign ROI, and attribution. Translate imperfect data into clear recommendations and continuously reallocate resources toward what is working.

Partner cross-functionally and influence investment: Build trusted relationships with Sales, Product Marketing, Marketing Operations, and RevOps while shaping channel and vendor spend and growing toward full budget ownership.

Essential Skills & Experience

4–7 years of experience in B2B demand generation or growth marketing, with a demonstrated track record of increasing pipeline.

Bachelor’s degree in Marketing, Communications, Business, or related field, or equivalent practical experience.

Strong quantitative and analytical skills, including comfort with funnel metrics, attribution, performance analysis, and making decisions from imperfect data.