Aifund
Engineer in Residence: MarketRadar
Mountain View, CA
No sponsorshipDetected 18 days ago
CybersecurityCRMSalesSupply Chain
About the role
- Category, pricing, product, and sales enablement teams need to understand fast-moving competitive changes across many products, configurations, price bands, channels, and geographies.
- The hard part is not collecting more data.
- Existing digital shelf and price monitoring products are built for a retail lens: price, promotion, availability, and assortment.
Responsibilities
- 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.
- Comfort building for enterprise buyers where security posture, audit trails, and deployment trust matter from day one.
- Experience building agentic monitoring, proactive alerting, eval harnesses, model routing, open-source model deployment, or token-cost optimization in production.
- This role is to build MarketRadar, an AI-native commercial action layer for SKU-heavy manufacturers.
- The hard part is turning fragmented market signals into a defensible recommendation about what to do with the company's own portfolio, pricing, positioning, inventory, and sales motion.
- The first wedge is for OEM commercial teams that manage complex hardware portfolios.
Requirements
- 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.
- Helpful but not required
- Experience with category planning, commercial strategy, pricing optimization, product taxonomy, SKU enrichment, win/loss analysis, or supply chain visibility.
- Evidence that you can use AI coding assistants and modern AI tools to move faster without outsourcing engineering judgment.
Compensation
- 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.
Benefits
- Equity consideration at founding-team terms if the venture spins out and you become a founder.
Company info
- US work authorization. We are unable to sponsor visas for this role.
- We are unable to sponsor visas for this role.
Visa & Work Authorization
- US work authorization.
This listing is sourced directly from Aifund's careers page and normalized into a canonical job model.