Founding Applied ML Engineer

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.