About the role

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 Kaushik Mahorker, founder of Wildcard. Previously at Scale AI, he 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 applied ML judgment to build reliable AI systems, ranking systems, evals, attribution models, agents, and automation loops that customers can trust. This is not a pure research role or pure analytics role or 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, expert-level with AI coding tools who uses AI to move faster but does not outsource judgment to it. This market is moving fast with AI shopping agents and agentic commerce protocols changing in real time; ambiguity is opportunity.

Week 0 projects

• Building custom ML models to classify prompts, predict opportunity, prioritize brand optimization
• Building incrementality and attribution systems connecting AI visibility to revenue outcomes
• Building prompt discovery systems identifying shopper queries across AI commerce surfaces
• Designing ranking/scoring/evaluation systems for noisy AI commerce outputs
• Modeling site traffic, conversion patterns from messy real-world data
• Making core AI workflows reliable with queues/retries/observability/evals/workflow orchestration
• Building agents recommending/executing/validating changes across ecommerce sites
• Designing pipelines to collect new signals into usable product intelligence
• Adapting product to emerging agentic commerce protocols/platform launches
• Migrating early systems into scalable product infrastructure without slowing execution

We’re looking for someone who

• Has prior founding experience or was early at Seed/Series A/B or similarly fast-moving company
• Has strong full-stack experience; can ship independently across stack
• Has applied ML or data science experience especially with LLMs/ranking/retrieval/evals/attribution/experimentation/product intelligence
• Can move between modeling/analysis/implementation/product decisions
• Is high-agency/self-directed; turns ambiguity into shipped product
• Expert-level with AI coding tools; uses them effectively without outsourcing judgment
• Can reason about model behavior/failure modes/quality without perfect data
• Moves fast; focuses on outcomes; does more with less
• Brings new ideas constantly; prioritizes granularly
• Resilient through changing priorities/new info/mini-pivots
• Excited by ownership/ambiguity/wearing multiple hats
• Wants tight feedback loops with customers
• High schlep tolerance; willing to do unglamorous work if it moves business forward
• Can push back/thinks independently yet moves quickly

Preferred experience

• Applied ML/data science/AI systems work in production or near-production environments
• Attribution modeling/traffic analysis/forecasting/causal inference/experimentation/product analytics
• Experience taking ML models from offline analysis to production systems customers use
• Data pipelines/instrumentation/signal collection from messy real-world sources
• Strong Python and SQL skills
• LLM workflows/retrieval systems/evals/fine-tuning/model evaluation
• AI agents including context management/orchestration/tool use/evals
• Ecommerce/marketplaces/search/recommendations/analytics/growth systems
• Enough full-stack experience to ship customer-facing products/APIs/internal tools (Typescript/Express/React)

Why join

You’ll work on problems between modeling/product/data infrastructure in a fast-paced practical environment tied directly to company priorities. Work goes from messy data to production product to customer impact quickly. You will help decide what gets built and see if it changes business outcomes.

About the interview

1. Pending Approval
2. Quick Call with Founder
3. Engineering Challenge
4. One to two-day work trial

Offer Extended

1. Hired