Lantern is looking for a Machine Learning Engineer to help build and own an end-to-end ML platform that turns operational data into reliable, scalable production recommendations. This is a founding-era, high-impact role in San Francisco where you will drive outcomes across hundreds of customers, with a tight-knit founding team and compensation around $200K base plus meaningful equity.

In this position, you will shape a platform that is scalable, repeatable, observable, and straightforward to operate. You will focus on building workflows that ingest data, train models, generate outputs, and continuously monitor performance, while improving pipeline reliability and performance as Lantern grows.

Responsibilities

Own and scale the ML and data pipelines that power Lantern 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 detect data-quality issues, pipeline failures, and unexpected outputs before they reach customers.
Convert customer-specific workflows into repeatable, configuration-driven systems and help guide platform architecture.

Requirements

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, AWS or GCP, and orchestration tools such as Airflow, Kubeflow, or similar.
Comfort working with messy, real-world data.
BS/MS in Computer Science or a related technical field.

Benefits

Founding-era, high-impact role at a hyper growth startup.
Own the ML platform that drives customer P&L on every account.
Make a major positive impact across Lantern’s supply chain and economy.
Competitive compensation: ~$200K base + meaningful equity.
Office in San Francisco with a tight-knit founding team.

Technologies

Python, AWS, GCP, Airflow, Kubeflow, XGBoost, LightGBM, scikit-learn

Nice to Have

Supply chain, logistics, demand forecasting, or operations-adjacent experience.
Time-series forecasting frameworks and traditional ML tools such as XGBoost, LightGBM, and scikit-learn.
Prior early-stage startup experience where you wore multiple hats.

What Lantern Values

Lantern hires operators who own outcomes, balancing 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 are central to the team.

Machine Learning Engineer