Wildcard (YC W25) is hiring an applied ML engineer
Hiring: Founding Applied ML Engineer @ Wildcard (YC W25)
Wildcard is seeking a Founding Applied ML Engineer to join their team in San Francisco. This is a high-impact role designed for a builder who can bridge the gap between raw machine learning research and a production-ready product.
📋 Role Overview
| Detail | Specification |
|---|---|
| Compensation | $130,000 — $250,000 |
| Equity | 0.50% — 4.00% |
| Location | San Francisco, CA, US |
| Type | Full-time |
| Experience | 3+ years |
| Eligibility | US Citizen or Visa holder |
🚀 About Wildcard
Wildcard is building the definitive agentic commerce optimization platform tailored for retail and e-commerce brands. As the paradigm of shopping evolves, the company provides the "mission control" necessary for brands to navigate the shift from traditional search to AI-driven discovery.
The Mission: To empower brands to understand, optimize, and monetize their presence across AI shopping agents through visibility (AEO/GEO), automated execution, and precise attribution.
The Market Shift
The way consumers find products is changing fundamentally:
👤 Who You'll Work With
You will report directly to Kaushik Mahorker, the founder of Wildcard.
Kaushik previously worked at Scale AI, where he spearheaded the e-commerce enrichment engine. This project involved managing:
- 400K SKUs
- 2.8M attributes
- Hundreds of distinct taxonomies
- Resulted in $15M+ in contracts with top-tier marketplaces and retailers.
🛠️ The Role & Expectations
As the Founding Applied ML Engineer, you aren't just writing code; you are shaping the company's DNA. You must be capable of owning the entire product engineering lifecycle while maintaining the ML rigor needed to build systems that customers trust.
Key Traits:
- High Agency: You don't
wait for a Jira ticketwait for someone to define your lane. - AI-Augmented: You are an expert with AI coding tools, using them to accelerate output without sacrificing critical judgment.
- Versatile: You move seamlessly between infrastructure, data science, and customer-facing product problems.
📅 "Week 0" Project Roadmap
If you join, your immediate focus will include:
- ML Modeling: Classifying prompts and predicting optimization opportunities for brands.
- Attribution Systems: Connecting AI visibility to actual revenue outcomes (e.g., ).
- Prompt Discovery: Identifying what shoppers are actually asking AI agents.
- Evaluation Frameworks: Designing scoring and ranking systems for noisy AI outputs.
- Data Engineering: Modeling conversion patterns from messy, real-world datasets.
- Reliability: Implementing queues, retries, and observability for AI workflows.
- Agentic Workflows: Building agents that can execute and validate site changes.
- Intelligence Pipelines: Turning new signals into actionable product insights.
- Scaling: Transitioning "scrappy" prototypes into robust production infrastructure.
🎯 Candidate Requirements
Must-Haves
- Experience: Prior founding experience or early-stage tenure (Seed Series B).
- Technical Breadth: Strong full-stack capabilities; able to ship independently.
- ML Expertise: Applied experience with LLMs, retrieval, ranking, and experimentation.
- Mindset: Resilient to pivots, comfortable with ambiguity, and possesses a high schlep tolerance (willing to do the unglamorous work).
- Judgment: Ability to reason about model failure modes without needing perfect data.
Preferred Technical Stack & Skills
| Category | Preferred Experience |
|---|---|
| Languages | Python, SQL, TypeScript, Express, React |
| AI/ML | LLM workflows, RAG, Fine-tuning, Model Evals, AI Agents |
| Analytics | Causal inference, Forecasting, Attribution modeling, Traffic analysis |
| Systems | Data pipelines, Instrumentation, Productionizing ML models |
| Domain | E-commerce, Marketplaces, Search, or Growth systems |
🌟 Why Join Wildcard?
This is not a role for someone who wants to spend six months optimizing a single hyperparameter in a vacuum. Instead, you will:
- Work at the Intersection: Sit exactly where modeling, product, and data infrastructure meet.
- Drive Immediate Impact: Move from messy data to a production feature to customer value in days, not months.
- Own the Strategy: Help decide what gets built and how it evolves as the agentic commerce market shifts.
