Product Manager, AI

About the role

The role defines and owns AI-powered product features for social commerce. It aligns product decisions with how brands build community through authentic social interactions and embed AI across the product experience. You will own the end-to-end lifecycle of AI features from discovery through launch, ensuring that every interaction strengthens community ties and drives commercial value. You will translate behavioral data into product decisions that make social engagement more productive and commercially viable for brands. This position requires you to act as the connective tissue between engineering, research, and brand teams to ensure AI capabilities solve real marketplace problems. You will set the product vision for AI while maintaining a sharp focus on user outcomes and retention in fast-moving social environments. Your work will establish the standards for how AI is integrated into the social shopping journey from concept to scale.

Key facts

What you'll do

Product specifications are written, features are tested, and quick iterations are run within a fast-moving startup environment to preserve momentum. Research gathers feedback and translates insights so social interactions build community value. Requirements are translated into clear product documentation that guides engineering toward outcomes that reinforce authentic social connections. Data is analyzed to identify patterns in how communities engage with commerce, and these insights shape the roadmap for AI features. Stakeholders are managed across technical and non-technical teams to ensure alignment on priorities for AI-powered social tools. Experiments are designed and executed to measure the impact of AI features on engagement, retention, and conversion within social shopping contexts. Ambiguity is navigated by defining the right questions and setting decision frameworks when outcomes are unknown. Prioritization is performed continuously so that the most valuable AI capabilities receive the appropriate focus in early-stage conditions. Cross-functional collaboration is coordinated so that engineering, product, and research teams refine AI experiences based on real user behavior.

Requirements

The posting states a bachelor's degree requirement. 5+ years of product management experience shipping successful software products is required in fast-paced settings. Strong analytical skills with experience defining metrics and using data to guide product decisions for AI features is required. Experience working with or a strong interest in AI/ML technologies, particularly generative AI or prompting workflows that shape workflows, is required. Excellent communication and stakeholder management skills across technical and non-technical teams are required to align execution. Projects are driven in high-ownership environments where ambiguity is common, and this capability is required. Comfort operating in ambiguity and prioritizing effectively in early-stage product environments is required.

Nice to have

Experience working at early-stage or high-growth startups that move quickly earns bonus points. A background in social commerce, creator economy tools, or enterprise SaaS products used by brand teams earns bonus points. Familiarity with machine learning systems such as LLMs, embedding systems, or recommendation engines that power personalization earns bonus points. Experience building products for consumer brands or marketing teams managing social engagement earns bonus points. An MBA or a track record of exceptional academic or professional achievement that demonstrates ownership earns bonus points.

Practical notes

The role includes comprehensive stipends such as a $1,000 monthly housing stipend near the office, a $50 mobile stipend, a $50 internet stipend for remote staff, and commuter benefits. 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

Social commerce shapes how brands build community through authentic social interactions. Generative AI drives personalization and workflow automation in modern commerce platforms. AI-native workflows combine prompting, models, and metrics to deliver measurable outcomes for users. Early-stage product environments require comfort with ambiguity and fast iteration to ship quickly. Cross-functional collaboration aligns engineering, product, and research around customer insights to refine experiences.

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.