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Supply chains power the world - but most distributors still rely on spreadsheets, gut instinct, and clunky ERP systems to make million-dollar purchasing decisions. We’re building Lantern, an AI-native purchasing copilot that helps wholesale distributors buy smarter.
$8T market, zero modern AI. Wholesale distribution is massive, fragmented, and underserved. Your algorithms won’t be polishing dashboards - they’ll steer billions in working capital.
Live pilots, real ARR. We already generate revenue with design partners who push our models in production every week. You’ll iterate on live feedback, not hypothetical use-cases.
End-to-end machine learning ownership. As founding engineer, you’ll design data pipelines, train and deploy ML models, and watch your code drive purchase orders that cut stock-outs and slash excess inventory by 50%, giving millions of dollars of cash back to each customer.
Proven tech + top-tier VC backing. Join founders with 10+ years of experience shipping large-scale ML products and ample funding from top-tier investors.
We’re a small, experienced founding team with a deep obsession for the problem. Between us, we’ve founded companies, shipped products at scale, and worked at places like Google, McKinsey, Stripe, and Walmart’s Applied AI Group. We studied at Stanford & IIT. Most importantly, we’re deeply aligned on speed, customer obsession, low-ego collaboration, and belief that a tiny team can transform an industry.
The RoleAs Lantern’s Founding AI Engineer, you’ll be the fifth person on the team and wear many hats. You’ll partner directly with the founders to design data pipelines, build and deploy ML models, and stand up MLOps that scales across customers. This isn’t just an engineering job - it’s a chance to shape the product, architecture, and company.
What You'll DoOwn product velocity across the full ML stack - from cloud infra and data pipelines to model-serving endpoints and (lightweight) dashboards
Ship ML features fast: prototype, demo to customers, iterate on feedback
Scale our forecasting engine and data ingestion workflows across customers, with automated retraining and drift monitoring
Design and build clean, scalable backends, APIs and inference services
Collaborate on architecture, product direction, hiring, and culture
Obsess over real-world outcomes: inventory turns, margin, cash flow
Prior startup experience (or serious appetite for it)
2+ years relevant work experience
Fluent in Python & SQL
A track record of shipping ML systems to production – from feature engineering and training to serving, monitoring, and retraining
Deep exposure to a modern cloud ML stack (we use GCP: BigQuery, Vertex AI, Cloud Composer)
Strong systems design instincts and a bias for building pragmatic, scalable solutions
High-agency, low-ego — you ship, you own, you care
Based in the Bay Area — in office 5 days/week
Hands-on with production MLOps (drift detection, model registries, feature stores, explainability)
Experience working with time-series and unstructured data for forecasting/optimization
Terraform & Kubernetes experience for infra-as-code and resilient deployments
A history of mentoring early hires and contributing to culture
Passion for supply chain, logistics, or the physical economy
Highly competitive salary and equity commensurate with 100X performer
Full health, dental, and vision insurance
Visa support available
Intangibles:
A true zero-to-one journey with direct line to customers, impact, and decisions
Mentorship from experienced founders and access to an elite investor network
Onsite collaboration that accelerates learning, velocity, and culture
The chance to help reinvent how an $8T industry operates
