Product Marketing Manager

About the role

Strategic positioning and market definition guide how Dexmate products reach enterprise customers. Cross-functional collaboration with product, sales, and leadership turns complex robotics into clear customer value.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Clear product differentiation emerges for Dexmate's robotics offerings, defining how they are positioned, communicated, and brought to market. Enterprise customers receive compelling, customer-facing value propositions that translate complex robotics and physical AI capabilities. Priority industry use cases and the Ideal Customer Profile are identified across target sectors to focus go-to-market efforts.

End-to-end product launches and comprehensive GTM planning are led to drive market adoption for new offerings. Strategic alignment is ensured through cross-functional partnership with Product, Sales, and Leadership teams. Priority verticals receive tailored industry messaging that addresses specific customer needs and workflows. High-impact sales materials such as pitch decks, one-pagers, ROI calculators, and competitive battlecards are developed to enable sales success. The full customer journey is supported through defined content, including case studies, demo narratives, and technical explainers.

Requirements

Candidates must bring 4-8+ years of product marketing experience in B2B enterprise software, robotics, AI/ML, developer ecosystems, or hardware-software platforms. Understanding complex robotics and AI concepts is required to communicate technical details effectively. Clear, compelling customer-facing messaging must be crafted from complex technical concepts. Ownership or contribution to go-to-market strategy and product launches is necessary. Collaboration with Product, Engineering, and Sales teams must be strong and consistent. Effective written and verbal communication and storytelling skills are essential. Thriving as a self-starter in a fast-paced, ambiguous startup environment is required.

Nice to have

Experience in robotics, automation, or industrial technology is preferred. Familiarity with highly technical products and engineering teams is valued. Understanding of long enterprise sales cycles and complex buying processes is preferred. Background in developing sales enablement materials and customer-facing content is preferred.

Practical notes

This role is based in the Fremont Office and operates full-time. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

The position shapes how enterprise customers understand and adopt complex robotics and physical AI technologies.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.