Machine Learning Engineer
Lantern
About the role
WHO WE ARE
Lantern is building the AI-native operating system for the $9T wholesale distribution industry. Billions of dollars sit tied up in stock, yet most distributors still buy on gut feel and spreadsheets. We partner with leading distributors to deliver precise, AI-powered purchasing recommendations that drive 30% leaner inventory, 10% higher sales, and 20+ hours saved per week per user. We've gone from $0 to $5M ARR in 8 months, raised $7M from top VCs, and are hitting hypergrowth.
THE ROLE
We're hiring a Machine Learning Engineer to scale the core ML platform behind our product: the production systems that ingest operational data, train models, and generate recommendations. The central challenge is making these workflows scalable, repeatable, observable, and easy to operate across hundreds of customers. You'll own the platform end to end, from data ingestion through training pipelines to inference and monitoring in production. It's a high-ownership role on a small team, working directly with our CTO to ship systems every customer relies on.
WHAT YOU'LL DO
Own and scale the ML and data pipelines that power Lantern's customer recommendations.
Build reusable infrastructure for data ingestion, feature generation, model training, validation, and output.
Improve pipeline performance, reliability, and observability as Lantern scales across more customers.
Build systems that catch data-quality issues, pipeline failures, and unexpected outputs before they reach customers.
Turn customer-specific workflows into repeatable, configuration-driven systems, and help shape the platform architecture.
WHAT YOU BRING
4–6 years as an ML or Software Engineer building ML systems in production.
Strong software engineering fundamentals and clean, maintainable, production-grade code.
Experience building and scaling ML training and inference pipelines, ideally multi-tenant or per-customer.
Hands-on with Python, cloud infrastructure (AWS or GCP), and orchestration (Airflow, Kubeflow, or similar).
Comfort with messy, real-world data.
BS/MS in Computer Science or a related technical field.
NICE TO HAVE
Supply chain, logistics, demand forecasting, or operations-adjacent experience.
Time-series forecasting frameworks and traditional ML tools (XGBoost, LightGBM, scikit-learn).
Prior early-stage startup experience where you wore multiple hats.
WHAT YOU'LL GET
A founding-era, high-impact role at a hyper growth startup.
Own the ML platform that drives customer P&L on every account.
Make a massive positive impact across our entire supply chain and economy.
Competitive compensation: ~$200K base + meaningful equity.
An office in San Francisco with a tight-knit founding team.
WHAT WE VALUE
We hire operators who own outcomes, combining sharp judgment with pragmatic execution and a willingness to do the unglamorous work. Hard work, humility, customer obsession, and pride in a job well done: the values our customers live by, and the values we hire for.
Responsibilities
- Own and scale the ML and data pipelines powering customer recommendations.
- Build reusable infrastructure for data ingestion, feature generation, model training, validation, and output.
- Improve pipeline performance, reliability, and observability.
- Detect data-quality issues, pipeline failures, and unexpected outputs before they reach customers.
- Turn customer-specific workflows into repeatable, configuration-driven systems and help shape platform architecture.
Qualifications
- 4–6 years as an ML or software engineer building production ML systems.
- Strong software engineering fundamentals and experience writing clean, maintainable, production-grade code.
- Experience building and scaling ML training and inference pipelines, ideally multi-tenant or per-customer.
- Hands-on experience with Python, AWS or GCP, and orchestration tools such as Airflow or Kubeflow.
- Comfort working with messy, real-world data.
- BS or MS in Computer Science or a related technical field.
Benefits
- Meaningful equity.
- High-impact role at a hypergrowth startup with ownership of the ML platform.
- San Francisco office and a close-knit founding team.
Skills mentioned
About Lantern
Lantern works with distributors to reduce inventory by 30-40%, improve fill rates by 5-10%, and free up cash for growth. We help distributors forecast demand so you always have the right items, in the right place, at the right time. We take a fundamentally different approach, using cutting-edge machine learning to achieve near-perfect accuracy. Our founders studied at Stanford and spent decades at leading Silicon Valley technology companies. Our families come from distribution. This combination of deep industry roots and frontier technology is what allows us to achieve breakthrough results. Lantern is honored to serve distributors, the true backbone of our economy and our country.
H-1B sponsorship history
Historical employer filing data was found for Lantern. The employer record includes 7 historical certified applications. This is employer-level history, not a guarantee that this role currently offers sponsorship.