This role is to build MarketRadar, an AI-native commercial action layer for SKU-heavy manufacturers. 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. 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. Existing digital shelf and price monitoring products are built for a retail lens: price, promotion, availability, and assortment. MarketRadar is built for the OEM lens: how a competitor's price, volume, configuration, channel, or portfolio move should change the OEM's own commercial action.AI Fund is working with an enterprise design partner on the problem. The residency is designed to test whether this can become a venture-scale company and whether you are the right technical founder to lead it.- This is an on-site Engineer in Residence residency at AI Fund headquarters in Mountain View, CA.- Residency length is typically 10 to 12 weeks. Compensation is paid for the duration of the residency.- The residency is designed to test founder-market fit. Strong performance is the path to a founding role at the spun-out venture.- You should be ready to ship a working prototype in weeks, not months.- Candidate-facing materials should not include enterprise partner confidential data, figures, slides, or internal analysis. Prototype work uses synthetic data.
This role is to build MarketRadar, an AI-native commercial action layer for SKU-heavy manufacturers. 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. 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. Existing digital shelf and price monitoring products are built for a retail lens: price, promotion, availability, and assortment. MarketRadar is built for the OEM lens: how a competitor's price, volume, configuration, channel, or portfolio move should change the OEM's own commercial action.AI Fund is working with an enterprise design partner on the problem. The residency is designed to test whether this can become a venture-scale company and whether you are the right technical founder to lead it.- This is an on-site Engineer in Residence residency at AI Fund headquarters in Mountain View, CA.- Residency length is typically 10 to 12 weeks. Compensation is paid for the duration of the residency.- The residency is designed to test founder-market fit. Strong performance is the path to a founding role at the spun-out venture.- You should be ready to ship a working prototype in weeks, not months.- Candidate-facing materials should not include enterprise partner confidential data, figures, slides, or internal analysis. Prototype work uses synthetic data.
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.
What you need
- 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. We are unable to sponsor visas for this role.
Helpful but not required
- 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.
Who this is for
- A technical founder who likes hard data and workflow problems where the product must make a decision, not just explain a chart.
- Someone who can reason about messy market signals, customer constraints, enterprise deployment, and product wedge at the same time.
- A builder who sees the opportunity to turn competitive intelligence into an OEM-native action system.