By Muslim 
Main Facts: The Emergence of the "Mission Control" for AI Shopping
The landscape of global e-commerce is undergoing its most radical transformation since the invention of the digital storefront. As consumer behavior pivots from traditional keyword-based search engines toward AI-driven shopping agents, a massive visibility gap has opened for retailers. Enter Wildcard, a high-growth platform positioning itself as the "mission control for agentic commerce."
Wildcard is currently disrupting the retail technology sector by providing brands with a comprehensive suite of tools to manage, optimize, and monetize their presence across emerging AI shopping ecosystems. The company, which is currently witnessing a staggering 50% month-over-month growth, has officially opened its search for its first-ever engineering hire: a Founding Applied ML Engineer. This individual will not merely be a cog in an existing machine; they will be the architect of the company’s foundational infrastructure, working directly alongside founder Kaushik Mahorker to shape the future of how products are discovered in an AI-first world.
Chronology: From Scale AI to the Future of Commerce
The vision for Wildcard was born from a realization triggered by massive scale. Founder Kaushik Mahorker previously spearheaded the e-commerce enrichment engine at Scale AI, a pivotal role that involved managing 400,000 SKUs and 2.8 million attributes across hundreds of complex taxonomies. This high-stakes environment resulted in over $15 million in contracts with major global retailers and marketplaces.
During this tenure, Mahorker observed a systemic shift: the traditional "search, click, purchase" funnel was becoming obsolete. As AI agents began to act as intermediaries between brands and consumers, the old rules of Search Engine Optimization (SEO) no longer applied. Brands were becoming invisible to the new gatekeepers of commerce.
Recognizing that most brands were woefully unprepared for this shift, Mahorker stepped away to found Wildcard. The mission was clear: build a platform that grants brands visibility (AEO & GEO—Agent Engine Optimization and Generative Engine Optimization), actionable recommendations, execution protocols, and attribution tracking—all within a single, unified interface.
Supporting Data: Why the Shift is Irreversible
The market demand for Wildcard’s technology is rooted in hard data. Consumer behavior is migrating rapidly toward "agentic" interactions—conversational interfaces that make purchasing decisions on behalf of users. When a consumer asks an AI, "Find me the best running shoes for a marathon under $150," the AI’s response is dictated by its underlying training and retrieval-augmented generation (RAG) processes.
Wildcard’s growth trajectory—50% month-over-month—is a testament to the urgency brands feel regarding this shift. The company’s value proposition is built on four pillars:
- Visibility: Identifying exactly how and why a brand appears (or fails to appear) in AI agent responses.
- Competitor Intelligence: Decoding why competitors are winning the "AI lottery" and securing placement.
- Optimization: Providing the "why" and "how" behind improving brand presence.
- Attribution: Closing the loop to ensure that AI-driven visibility translates into tangible business outcomes and revenue.
The Role: A Founding Engineer for an AI-First Era
The search for a Founding Applied ML Engineer represents a pivotal moment in Wildcard’s history. The company is explicitly looking for a "builder" rather than a researcher. The role is designed for a high-agency individual who can navigate the entire stack, from low-level data infrastructure to high-level customer-facing product features.
A Hybrid Breed of Engineer
The ideal candidate is expected to occupy a rare intersection of skills. They must be capable of:
- Applied ML Judgment: Building reliable, trustworthy AI systems, ranking algorithms, evaluation frameworks, and attribution models.
- Full-Stack Ownership: Being comfortable working across the stack to ensure that ML models are not just "lab experiments" but production-ready, mission-critical assets.
- Infrastructure Management: Architecting the data pipelines that feed the AI engines, ensuring scalability and speed as the company continues its rapid growth.
- Product Vision: Contributing to the strategy, prioritization, and road-mapping of the product.
This is not a role for those who prefer to operate in a silo. The Founding Engineer will work directly with customers, translating real-world retail problems into technical solutions. They are expected to be an expert in AI coding tools, utilizing them to accelerate development velocity without sacrificing human judgment or code quality.
Official Response: The "Ambiguity is the Opportunity"
In describing the role, founder Kaushik Mahorker emphasizes that the current state of the market is defined by "ambiguity." For many, this uncertainty is a risk; for Wildcard, it is the primary competitive advantage.
"We are not looking for someone to wait for a roadmap to be handed to them," says Mahorker. "We are looking for someone to define the lane. We need a partner who can bridge the gap between messy, real-world data and a production product that impacts millions of dollars in revenue."
The company stresses that this role is designed for someone who wants to move fast. "You won’t spend months optimizing one narrow model in isolation," the company notes. "You’ll ship features from end to end, observe their impact, and iterate. You will be able to point to the specific systems you built as the reason why we win in the market."
Implications: What This Means for the Future of Retail
The hiring of a Founding Applied ML Engineer at Wildcard has significant implications for the broader retail industry. It signals that we have officially entered the "Agentic Era" of commerce.
The Death of Traditional Search
As AI agents become the primary interface for shopping, the entire retail industry is facing a "black box" problem. Brands are realizing that if they aren’t optimized for the nuances of Large Language Models (LLMs) and agentic protocols, they risk disappearing entirely from the consumer’s radar. Wildcard’s emergence provides a lifeline, turning a chaotic, algorithmic landscape into a manageable dashboard.
The Rise of the "Generalist Builder"
The requirement for this role reflects a broader shift in the tech hiring landscape. Companies in the AI space are increasingly prioritizing "generalist builders"—engineers who possess enough ML expertise to build sophisticated models, but enough product sense to understand the business value of those models. The separation between "Product Engineering" and "Data Science" is rapidly collapsing, and Wildcard is at the forefront of this organizational evolution.
The Competitive Landscape
As Wildcard builds its "mission control," the implications for their competitors are stark. Brands that utilize Wildcard will gain a systematic advantage in how they are represented in generative search. By successfully recruiting an engineer capable of turning data into reliable business outcomes, Wildcard is effectively setting the standard for how the next generation of retail software will be built.
Conclusion: A Rare Opportunity to Shape the Stack
For the right candidate, the Founding Applied ML Engineer position at Wildcard offers more than just a job; it offers the chance to define the operational backbone of modern e-commerce. As shopping continues its migration from static pages to dynamic, agentic interactions, the systems that govern this transition will become some of the most valuable software in the world.
Wildcard is not just building a tool; they are building the infrastructure that will determine which brands win and which brands fade in the AI-first economy. The founding engineer will be at the epicenter of this shift, turning the ambiguity of today’s AI market into the foundational systems of tomorrow.
For those interested in the role, Wildcard is actively seeking individuals with high agency, a history of shipping end-to-end products, and a deep, practical understanding of applied machine learning. This is an invitation to build at the bleeding edge of the intersection between retail, AI, and data infrastructure.
Iffa Jayyana 
Lina Irawan 
Jia Lissa 