ML Platform Engineer — End-to-End Production Pipelines & Equity

Lantern; San Francisco (CA); On-site; USD 190,000 - 210,000; Full time

Benefits offered by this job

Equity

Office in San Francisco

Competitive salary

Job summary

Lantern is seeking a Machine Learning Engineer in San Francisco to scale the core ML platform behind our product. You will own the data ingestion, training pipelines, and the end-to-end ML workflow that generates customer recommendations used across all accounts.

We expect 4–6 years in ML or software roles, strong production-grade coding, and hands-on experience with Python, AWS or GCP, and orchestration tools like Airflow or Kubeflow. This is an in-office role with a tight-knit founding team.

Qualifications

• 4–6 years of experience building ML systems in production.
• Proficient in clean, maintainable, production-grade code.
• Experience with ML training and inference pipelines, ideally multi-tenant.

Responsibilities

• 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.

Skills

ML systems in production

Python

Data pipelines

Cloud infrastructure

Airflow/Kubeflow

Software engineering

Multi-tenant ML

Education

BS/MS in Computer Science or related field

Tools

Airflow

Kubeflow

AWS

GCP

Job description

Lantern is seeking a Machine Learning Engineer in San Francisco to scale the core ML platform behind our product. You will own the data ingestion, training pipelines, and the end-to-end ML workflow that generates customer recommendations used across all accounts.

We expect 4–6 years in ML or software roles, strong production-grade coding, and hands-on experience with Python, AWS or GCP, and orchestration tools like Airflow or Kubeflow. This is an in-office role with a tight-knit founding team.