Senior ML Platform Engineer: Scale & Deploy ML

Jobgether; United States; Remote; USD 180,000 - 240,000; Full time

Job summary

Jobgether is seeking an experienced ML Platform Engineer in the United States to lead platform engineering initiatives covering the full ML lifecycle. You will design, develop, deploy, and operate scalable ML infrastructure, including model serving, feature computation, and monitoring, while collaborating with Data Science, Product, Risk, Fraud, and Engineering teams.

The role emphasizes production ownership, strong software fundamentals, and the ability to drive end-to-end projects with a focus

Qualifications

• At least 5 years of production software experience with Python.
• Strong fundamentals: API design, distributed systems, testing, maintainable code.
• Experience designing and operating cloud-native services in production.
• Hands-on ML platform, model serving, data platforms, or MLOps.
• Familiarity with feature engineering, model governance, deployment, and monitoring.

Responsibilities

• Lead ML platform engineering initiatives: Design, develop, deploy, and operate complex platform capabilities.
• Build scalable ML infrastructure for model deployment, serving, feature computation, data validation, orchestration, monitoring, and production support.
• Improve platform reliability and performance through reusable components, standards, automation, and cost efficiency.
• Own technical projects end to end, translating business needs into technical designs and coordinating delivery.

Skills

Python programming

API design

Distributed systems

Cloud-native design

Communication & collaboration

Tools

Docker

Kubernetes

CI/CD

Git workflows

Production observability tools

SQL

Job description

Jobgether is seeking an experienced ML Platform Engineer in the United States to lead platform engineering initiatives covering the full ML lifecycle. You will design, develop, deploy, and operate scalable ML infrastructure, including model serving, feature computation, and monitoring, while collaborating with Data Science, Product, Risk, Fraud, and Engineering teams.

The role emphasizes production ownership, strong software fundamentals, and the ability to drive end-to-end projects with a focus