About Wildcard

AEO/GEO for E-Commerce and Retail

Tech description:

Job description:

# **Founding Applied ML Engineer** ## **About Wildcard** Wildcard is the agentic commerce optimization platform for ecommerce and retail brands. We help brands understand, improve, and monetize how their products show up across AI shopping agents. We’re building the **mission control for agentic commerce**: visibility (AEO & GEO), recommendations, execution, attribution, and automation in one platform. As shopping shifts from traditional search to AI agents, brands need to know where they appear, why competitors are winning, what to change, and whether those changes drive real business outcomes. We’re growing **50% month over month**. ## **Who you’ll work with** You’ll work directly with me, Kaushik Mahorker, founder of Wildcard. Previously at Scale AI, I built the ecommerce enrichment engine behind the company’s largest pilot across 400K SKUs, 2.8M attributes, and hundreds of taxonomies, helping secure $15M+ in contracts with major retailers and marketplaces. That experience made something clear: shopping discovery is being rebuilt for an AI-first world, and most brands are not prepared for the shift. ## **The role** We’re looking for a Founding Applied ML Engineer to help shape both the product and the company from the earliest stage. This is engineer number one. You are not joining an engineering team. You are helping build one. The ideal person is strong enough to own product engineering across the stack, but also has the applied ML judgment to build reliable AI systems, ranking systems, evals, attribution models, agents, and automation loops that customers can actually trust. This is not a pure research role. It is not a pure analytics role. It is not a narrow full-stack role either. We need a builder who can move between product, infrastructure, applied ML, data, and customer problems without waiting for someone else to define the lane. You’ll work directly with customers, own product and infrastructure, and help decide what gets built, how it gets built, and what we prioritize as the market evolves. We are looking for someone high-agency, fast-moving, and expert-level with AI coding tools. You should use AI to move significantly faster, but not outsource your judgment to it. This market is moving fast. AI shopping agents, agentic commerce protocols, and consumer behavior are all changing in real time. The ambiguity is the opportunity. ## **Week 0 projects** You may work on: * Building custom ML models to classify prompts, predict opportunity, and prioritize what brands should optimize for * Building incrementality and attribution systems that connect AI visibility to revenue outcomes for ecommerce brands * Building prompt discovery systems that identify and predict what shoppers are asking across AI commerce surfaces * Designing ranking, scoring, and evaluation systems for noisy AI commerce outputs * Modeling site traffic, conversion patterns, and performance trends from messy real-world data * Making core AI workflows reliable with queues, retries, observability, evals, and workflow orchestration * Building agents that can recommend, execute, and validate changes across ecommerce sites * Designing pipelines to collect new signals and turn them into usable product intelligence * Adapting the product to emerging agentic commerce protocols and platform launches * Migrating scrappy early systems into scalable product infrastructure without slowing down execution ## **We’re looking for someone who** * Has prior founding experience, or was early at a Seed, Series A, Series B, or similarly fast-moving company * Has strong full-stack experience and can ship independently across the stack * Has applied ML or data science experience, especially with LLMs, ranking, retrieval, evals, attribution, experimentation, or product intelligence * Can move between modeling, analysis, implementation, and product decisions * Is high-agency, self-directed, and able to turn ambiguity into shipped product * Is expert-level with AI coding tools and uses them to move significantly faster * Has strong judgment on when to use AI and when not to * Can reason about model behavior, failure modes, and quality without needing perfect data * Moves fast, focuses on outcomes, and knows how to do more with less * Brings new ideas constantly and can prioritize at a granular level * Is resilient through changing priorities, new information, and mini-pivots * Gets excited by ownership, ambiguity, and wearing multiple hats * Wants to work in tight feedback loops with customers * Has high schlep tolerance and is willing to do unglamorous work when it moves the business forward * Can push back, think independently, and still move quickly ## Preferred experience * Applied ML, data science, or AI systems work in production or near-production environments * Attribution modeling, traffic analysis, forecasting, causal inference, experimentation, or product analytics * Experience taking ML models from offline analysis to production systems customers actually use * Data pipelines, instrumentation, and signal collection from messy real-world sources * Strong Python and SQL skills * LLM workflows, retrieval systems, evals, fine-tuning, and model evaluation * AI agents, including context management, orchestration, tool use, and evals * Ecommerce, marketplaces, search, recommendations, analytics, or growth systems * Enough full-stack experience to ship customer-facing product, APIs, or internal tools when needed (Typescript, Express, React) ## **Why join** You’ll work on problems that sit between modeling, product, and data infrastructure. The work is fast-paced, practical, and tied directly to company priorities. You will not spend months optimizing one narrow model in isolation. This is a rare applied ML role where the work goes from messy data to production product to customer impact quickly. You’ll help decide what gets built, ship it end to end, and see whether it actually changes business outcomes. You’ll be able to point to the models, systems, and product decisions you made as part of the reason why we win.

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Founding Engineer

Founding Applied ML Engineer