Machine Learning Engineer

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 hiring a Machine Learning Engineer (onsite) in San Francisco to build and own end-to-end ML platform workflows for customer recommendations.

Responsibilities

• Own and scale the ML and data pipelines that power Lantern’s customer recommendations.
• Build reusable infrastructure covering data ingestion, feature generation, model training, validation, and production output.
• Increase pipeline performance, reliability, and observability as Lantern grows 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 help shape platform architecture.

Requirements

• 4–6 years 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 in multi-tenant or per-customer setups.
• Hands‑on experience 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.

Technologies

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

Benefits

• Founding-era, high-impact role at a hyper growth startup.
• Opportunity to own the ML platform that drives customer P&L across every account.
• Meaningful impact across the supply chain and economy.
• Competitive compensation: ~$200K base + meaningful equity.
• San Francisco office with a tight‑knit founding team.

Nice to Have

• Supply chain, logistics, demand forecasting, or operations-adjacent experience.
• Experience with time-series forecasting frameworks and traditional ML tools like XGBoost, LightGBM, and scikit-learn.
• Prior early-stage startup experience with multiple responsibilities.

What We Value

• Operate with ownership of outcomes, combining sharp judgment with pragmatic execution.
• Willingness to do the unglamorous work.
• Hard work, humility, customer obsession, and pride in delivering a job well done.

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