ML Platform Engineer - End-to-End Pipelines (SF Onsite)

Lantern; San Francisco (CA); On-site; USD 180,000 - 220,000; Full time

Benefits offered by this job

Office in San Francisco

Job summary

Lantern is seeking a Machine Learning Engineer to own end-to-end ML platform workflows for customer recommendations. This onsite role in San Francisco involves building scalable data pipelines, reusable infrastructure, and production-grade code to deliver reliable, high-quality recommendations.

The ideal candidate has 4–6 years of experience in ML or software engineering, strong Python skills, and experience with AWS or GCP, Airflow, Kubeflow, and real-world data.

Qualifications

• 4–6 years of experience building ML systems in production.
• Strong software engineering fundamentals with production-grade code.
• Experience building and scaling ML training/inference pipelines (multi-tenant or per-customer).
• Hands-on Python, AWS or GCP, and orchestration tools like Airflow or Kubeflow.

Responsibilities

• Own and scale ML and data pipelines powering Lantern’s customer recommendations.
• Build reusable infrastructure for data ingestion, feature generation, model training, validation, and production outputs.
• Improve pipeline performance, reliability, and observability as Lantern grows.
• Implement safeguards to detect data-quality issues and pipeline failures before reaching customers.
• Convert customer-specific workflows into repeatable, configuration-driven systems and help shape platform architecture.

Skills

Python

AWS

GCP

Airflow

Kubeflow

ML pipelines

Clean code

Data wrangling

Education

BS/MS in CS

Job description

Lantern is seeking a Machine Learning Engineer to own end-to-end ML platform workflows for customer recommendations. This onsite role in San Francisco involves building scalable data pipelines, reusable infrastructure, and production-grade code to deliver reliable, high-quality recommendations.

The ideal candidate has 4–6 years of experience in ML or software engineering, strong Python skills, and experience with AWS or GCP, Airflow, Kubeflow, and real-world data.