About wildcards
Wildcard is an agentic commerce optimization platform for ecommerce and retail brands.
We help brands understand, improve, and monetize how their products appear to AI shopping agents. we are building Mission Control for Agentic Commerce: Visibility (AEO and GEO), recommendations, execution, attribution and automation on a single 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 are driving real business results.
we are growing 50% month to month.
who will you work with
Kaushik Mahorkar, Founder of Wildcard, you will work directly with me.
At the first Scale AI, I built the ecommerce promotion 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 reinvented for an AI-first world, and most brands are unprepared for this change.
Role
We are looking for a founding Applied ML Engineer to help shape both the product and the company from its early stages.
This is engineer number one. You are not joining an engineering team. You’re helping to build it.
The ideal person is strong enough to fully own product engineering, but also has the applied ML judgment to build reliable AI systems, ranking systems, evals, attribution models, agents, and automation loops that customers can truly trust.
This is not a pure research role. This is not a purely analytical role. This 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, your product and infrastructure, and help decide what to build, how to build it, and what we’ll prioritize as the market evolves.
We’re looking for someone high-agency, smart, and expert-level with AI coding tools. You should use AI to move much faster, but not outsource your decision making to it.
This market is growing rapidly. AI shopping agents, agentic commerce protocols, and consumer behavior are all changing in real time. Ambiguity is opportunity.
Week 0 Projects
You can work on:
- Building custom ML models to classify signals, 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 quick search systems that identify and predict what shoppers are asking on AI commerce surfaces
- Designing ranking, scoring, and evaluation systems for noisy AI commerce outputs
- Modeling site traffic, conversion patterns, and performance trends from real-world messy data
- Making core AI workflows reliable with queues, retries, observability, evaluation, and workflow orchestration
- Building agents that can recommend, implement, and verify changes on ecommerce sites
- Designing pipelines to collect new signals and transform them into useful product intelligence
- Product Adoption for Emerging Agentic Commerce Protocols and Platform Launches
- Moving redundant initial systems to scalable product infrastructure without slowing down performance
We are looking for someone who
- Have prior founding experience, or had early experience at a Seed, Series A, Series B, or similar fast-growing company
- It has a strong full-stack experience and can ship independently across the entire stack
- Have applied ML or data science experience, specifically with ranking, retrieval, valuation, attribution, experimentation, or product intelligence
- Can move between modeling, analysis, implementation, and product decisions
- High-agency, self-directed, and able to turn ambiguity into a shipped product
- AI coding tools are expert level and use them to progress significantly faster
- Has a firm decision on when to use AI and when not to
- Can reason about model behavior, failure modes, and quality without needing accurate data
- Moves fast, focuses on results and knows how to do more with less
- Constantly brings new ideas to the table and can prioritize at a detailed level
- Is flexible through changing priorities, new information, and mini-pivots
- Gets excited about ownership, ambiguity and wearing many hats
- Wants to work in a tight feedback loop with customers
- Has high scholarly tolerance and is willing to commit unethical acts when it furthers business
- Can push back, think independently and still move forward quickly
favorite 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 analysis
- Experience taking ML models from offline analysis to production systems actually used by customers
- Data pipelines, instrumentation, and signal collection from real-world chaotic sources
- Strong Python and SQL skills
- LLM Workflow, Retrieval System, Evaluation, Fine-Tuning and Model Evaluation
- AI agents, including context management, orchestration, tool usage, and evaluation
- Ecommerce, marketplaces, search, recommendations, analytics, or development systems
- Sufficient full-stack experience (TypeScript, Express, React) to ship customer-facing products, APIs, or internal tools when needed
Why join?
You will work on problems at the intersection of modeling, product, and data infrastructure.
The work is fast-paced, hands-on, and directly linked to company priorities. You won’t spend several months optimizing a narrow model in isolation.
This is a rare applied ML role where work moves rapidly from messy data to producing product to customer impact. You’ll help decide what to build, send it from start to finish, and see if it really changes business results.
You’ll be able to point to the model, system and product decisions we made as the reason we won.
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