Machine Learning Engineer

Lantern is looking for a Machine Learning Engineer to own end-to-end machine learning platform workflows, from ingesting operational data to training models, generating production recommendations, and monitoring performance. The role is onsite in San Francisco and focuses on scaling pipelines that are repeatable, observable, and straightforward for teams to operate across hundreds of customers.

Role Summary

In this position, you will build and improve ML and data pipelines that deliver customer recommendations. You will turn customer-specific workflows into reusable, configuration-driven systems while helping shape the platform architecture. As Lantern grows, the work centers on making pipeline execution scalable, reliable, and easier to operate through strong observability and data-quality protections.

Responsibilities

• Own and scale the ML and data pipelines that support Lantern's customer recommendations.
• Develop reusable infrastructure spanning data ingestion, feature generation, model training, validation, and output.
• Enhance pipeline performance, reliability, and observability as Lantern expands to more customers.
• Implement safeguards to detect data-quality issues, pipeline failures, and unexpected outputs before they reach customers.
• Convert customer-specific workflows into repeatable, configuration-driven systems and contribute to overall platform architecture.

Requirements

• 4 to 6 years of experience as an ML or Software Engineer building ML systems in production.
• Strong software engineering fundamentals with 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, cloud infrastructure (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.

Required Technologies

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

Nice to Have

• Experience in supply chain, logistics, demand forecasting, or operations-adjacent work.
• Familiarity with time-series forecasting frameworks and traditional ML tools including XGBoost, LightGBM, and scikit-learn.
• Early-stage startup experience where you handled multiple responsibilities.

What Lantern Values

Lantern emphasizes operators who take ownership of outcomes, apply sharp judgment with pragmatic execution, and handle the unglamorous work. They also highlight values such as hard work, humility, customer obsession, and pride in delivering a job well done.

Compensation and Location

• Location: San Francisco, CA (onsite)
• Salary: USD 200,000 per year
• Compensation Range: $200K

Benefits

• A founding-era, high-impact role at a hyper growth startup.
• Ownership of the ML platform that drives customer P&L on every account.
• Opportunity to make a significant positive impact across the supply chain and economy.
• Competitive compensation: approximately $200K base plus meaningful equity.
• An office in San Francisco with a tight-knit founding team.