Machine Learning Engineer

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.