Staff ML Platform Engineer — Build Self-Serve ML at Scale

Tilt; United States; A distancia; USD 150.000 - 210.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

W F H reimbursement

Equity/stock options

Health plans

Leadership exposure

Global offsites

Descripción de la vacante

Tilt is a remote-first company building a new credit system and powering millions of customers worldwide. As Staff Machine Learning Engineer, ML Platform, you’ll own the platform layer between data science and infrastructure, run our feature store, and build training/deployment pipelines that let data scientists ship faster with less engineering handoffs.

This role emphasizes self-service tooling, scalable inference, and standardized model/version governance across teams, with collaboration

Formación

• Hands-on platform/ML software engineering experience.
• Experience with feature stores and deployment platforms.
• Strong CI/CD and IaC practices for ML systems.

Responsabilidades

• Own the platform layer between data science and infrastructure.
• Run and improve the managed feature store and training/deployment pipelines.
• Build self-service tooling to accelerate data scientists’ work.
• Standardize model versioning, retraining, monitoring, and governance.
• Scale batch and online inference with cost-aware autoscaling.

Conocimientos

Platform engineering

ML platform

Feature store

Model deployment

CI/CD for ML

Cloud (AWS/Azure)

Educación

Advanced degree in CS/ML or related field

Herramientas

Databricks Feature Store

Feast

Tecton

Terraform

Kubernetes

GitHub Actions

Descripción del empleo

Tilt is a remote-first company building a new credit system and powering millions of customers worldwide. As Staff Machine Learning Engineer, ML Platform, you’ll own the platform layer between data science and infrastructure, run our feature store, and build training/deployment pipelines that let data scientists ship faster with less engineering handoffs.

This role emphasizes self-service tooling, scalable inference, and standardized model/version governance across teams, with collaboration