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This listing was closed on Jul 17, 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: ziprecruiter.com). Apply on the employer's site (ziprecruiter.com).
Imported listing (source: ziprecruiter.com) · Apply on ziprecruiter.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 ziprecruiter.com
Docker is hiring a Staff ML Engineer to join the Intelligence team, building ML-driven governance and security capabilities for Docker's platform. The role involves designing and shipping ML systems for tasks like prompt injection detection, anomaly detection, and trust scoring. The position is remote-first and offers the opportunity to shape technical direction and build foundational infrastructure.
Responsibilities
- Design, train, evaluate, and ship ML systems that power governance and security capabilities
- Build supporting infrastructure: data pipelines, feature stores, model serving, evaluation harnesses
- Make pragmatic build-vs-buy calls
- Set technical direction for the team's ML work
- Help recruit, mentor, and shape the team
Requirements
- 5+ years of deep applied ML/AI expertise with production systems
- 8+ years of professional software engineering experience in backend/infrastructure
- Bachelor's degree in CS or equivalent
- Experience building and owning ML systems (data pipelines, serving, evaluation)
- Fluency with modern AI tools and instinct for frontier models vs traditional ML
- Experience with LLM-based systems in production (evaluation, prompt engineering, fine-tuning)
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.
