Engineer in Residence: MarketRadar

What you'll build

• A synthetic but realistic market-signal pipeline that ingests competitor portfolio data, channel movement, sell-out velocity, pricing, and configuration changes.
• A spec-aware entity resolution system that maps competitor SKUs and portfolio restructures against the customer's own lineup without relying on name matching.
• A recommendation engine that turns detected changes into commercial actions such as monitor, promote an adjacent SKU, adjust price, update positioning, flag a portfolio gap, or investigate a future configuration change.
• A constraint layer that makes recommendations respect the customer's own cost, margin, inventory, channel, and sales enablement realities.
• A monitoring and alerting workflow that can run continuously, surface changes proactively, and improve from user feedback.

What you'll do

• Own the technical build from data model and ingestion through entity resolution, signal detection, recommendation logic, and operator workflow.
• Work directly with AI Fund's build team and enterprise users to pressure-test the wedge, prototype, and customer workflow.
• Decide which workflow ships first: competitive portfolio remapping, volume/configuration traction detection, win/loss intelligence, sentiment mining, or supply chain and lead-time signals.
• Build evaluation loops for recommendation correctness, false remaps, hallucinated spec drivers, and constraint failures.
• Design for enterprise trust from the beginning, including data isolation, sandboxed agents, auditability, and cost-conscious model routing.
• Move quickly from prototype to a pilotable system while keeping the core technical judgment explicit.

Requirements

• Strong hands-on engineering ability across backend systems, data products, and AI-native workflow software.
• Experience with messy structured or semi-structured data, such as product catalogs, SKU normalization, taxonomy mapping, pricing data, GTM data, sales enablement systems, CRM data, or supply chain signals.
• Judgment about entity resolution, recommendation systems, monitoring, evaluation, and model orchestration.
• Comfort building for enterprise buyers where security posture, audit trails, and deployment trust matter from day one.
• Evidence that you can use AI coding assistants and modern AI tools to move faster without outsourcing engineering judgment.
• Founder-level curiosity about the customer workflow, not just the model or dashboard.
• US work authorization.

Nice-to-haves

• Experience in OEM, manufacturing, consumer electronics, commerce infrastructure, retail pricing, catalog systems, market intelligence, competitive intelligence, sales intelligence, RevOps data products, or channel analytics.
• Experience building agentic monitoring, proactive alerting, eval harnesses, model routing, open-source model deployment, or token-cost optimization in production.
• Experience with category planning, commercial strategy, pricing optimization, product taxonomy, SKU enrichment, win/loss analysis, or supply chain visibility.
• Founder, founding engineer, or senior IC experience in a B2B software company.