ML Platform Engineer: Scale AI Infra & Pipelines

United States Digital Space LLC; Los Angeles (CA); Hybrid; USD 190,000 - 240,000; Full time

Benefits offered by this job

Equity (RSUs)

Job summary

United States Digital Space LLC is seeking a Machine Learning Platform Engineer to design, build, and scale our ML/AI infrastructure. You will work with AI/ML engineers to deploy reliable, scalable systems that support model development, deployment, and observability across multiple products and teams.

You will own core platform components, foster reusable patterns, and collaborate with stakeholders to meet SLAs while applying AI-native practices to improve efficiency and outcomes.

Qualifications

• 5+ years of software engineering experience.
• Experience designing infrastructure for machine learning lifecycles.
• Experience with at least one major cloud platform (AWS preferred).

Responsibilities

• Build core components of our ML and AI Platform roadmap to design and build MLOps solutions with automated pipelines and standardized processes to build, deploy, run, monitor, debug, and retrain ML and AI models.
• Develop, maintain, and enhance frameworks for ML model development and deployment.
• Collaborate with ML/AI builders and application owners to determine business requirements and SLAs for API-enabled services.
• Develop, maintain, and enhance infrastructure supporting ML services.
• Support the development of new patterns for deployment of ML models with CI/CD pipelines and automated testing.
• Apply AI tools as a regular part of your engineering workflow and bring an AI-native lens to decisions.
• Adopt the latest best practices for using AI technologies across all aspects of technical development

Skills

Python

Ruby

Java

Tools

AWS

Job description

United States Digital Space LLC is seeking a Machine Learning Platform Engineer to design, build, and scale our ML/AI infrastructure. You will work with AI/ML engineers to deploy reliable, scalable systems that support model development, deployment, and observability across multiple products and teams.

You will own core platform components, foster reusable patterns, and collaborate with stakeholders to meet SLAs while applying AI-native practices to improve efficiency and outcomes.