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This listing was closed on Jul 18, 2026.
Browse Open JobsStaff ML Engineer
Summary by AIEngineer.careers
Staff ML Engineer is a Machine Learning Engineer role (full-time). with Docker. in REMOTE, WORLDWIDE. Imported listing (source: monster.com). Apply on the employer's site (monster.com).
Imported listing (source: monster.com) · Apply on monster.com
REMOTE · WORLDWIDE
—
Full-time
STAFF
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from monster.com
Docker is building intelligence-driven product capabilities to make software and agent execution safer and more efficient. As a Staff ML Engineer on the Intelligence team, you will design, train, and ship ML systems for governance and security, including prompt injection detection and anomaly detection. You will build data pipelines, evaluation harnesses, and set technical direction for the team.
Responsibilities
- Design, train, evaluate, and ship ML systems for governance and security capabilities.
- Build data pipelines, feature stores, model serving, evaluation harnesses, and feedback loops.
- Make build-vs-buy decisions, using frontier models and custom systems as needed.
- Set technical direction for ML work, owning architecture and model lifecycle.
- Recruit, mentor, and shape the team as it grows.
- Participate in 24/7 on-call rotation for the Agentic Platform.
Requirements
- 5+ years of deep applied ML/AI expertise with a track record of shipping production systems.
- 8+ years of software engineering experience in backend, infrastructure, or platform engineering.
- Bachelor's degree in Computer Science or equivalent practical experience.
- Experience building and owning systems around ML models (data pipelines, serving, evaluation, monitoring).
Docker
San Francisco · US · 1055+ employees
Docker is a platform that simplifies the development, deployment, and management of containerized applications. It provides integrated tools for developers to build and share software, and now focuses on governance and security for AI-native development.
