Remote positions

Remote Docker Jobs

Browse remote Docker positions — containerisation for AI systems, MLOps infrastructure, and DevOps engineering at AI-first companies.

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Remote worldwide
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MLOps Engineer DevOps for AI Platform Engineer Infrastructure Engineer Containerisation Specialist

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Jobs & Projects

Neo4j

Posted 4 hours ago

Lead Technical Onboarding Engineer

Neo4j

Remote: India

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lead
Full-time
English
Full page
Wayve

Posted 1 day ago

Application Software Engineer

Wayve

Tokyo, Japan · also open in 2 other locations

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Full-time
English
Full page
Wayve

Posted 1 day ago

Principal Application Software Engineer

Wayve

Tokyo, Japan

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principal
Full-time
English
Full page
Wayve

Posted 1 day ago

Staff Platform Engineer, CI/CD

Wayve

Tokyo, Japan

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staff
Full-time
English
Full page
Wayve

Posted 1 day ago

Senior Platform Engineer, Cloud Infrastructure

Wayve

Tokyo, Japan

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senior
Full-time
English
Full page
Hyperbolic

Posted 2 days ago

Technical Support Engineer - APAC

Hyperbolic

Remote

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Contract
English
Full page
Turing

Posted 3 days ago

Software Engineer

Turing

Global - Remote; United States

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Full-time
English
Full page
Agility Robotics

Posted 4 days ago

Senior Release Manager

Agility Robotics

Remote

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senior
Full-time
English
$128K – $199K / year
Full page
Snowflake

Posted 4 days ago

Senior Solutions Engineer, Observe

Snowflake

US-NY-New York · also open in 1 other location

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senior
Full-time
English
$165K – $217K / year
Full page
Gradient AI

Posted 4 days ago

Staff Data Scientist

Gradient AI

Remote

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staff
Full-time
English
deep learning python docker mlops llm +2 more
Full page

Role guide

What to expect in remote Docker jobs

Docker is the foundation of modern AI deployment infrastructure. Remote Docker jobs at AI companies involve containerising ML models, building reproducible training environments, and managing deployment pipelines. Engineers with Docker experience who also understand ML workflows are particularly in demand.

What you'll work on

  • Containerise ML models for reproducible, portable deployment
  • Build multi-stage Docker images that keep model serving images lean
  • Create development environments that match production exactly
  • Orchestrate AI microservices across training and inference containers

What companies look for

  • Multi-stage Dockerfile design for ML workloads
  • Docker Compose for local development and testing environments
  • Experience with GPU container configurations for model training

Frequently asked questions

What Docker skills do AI companies typically require?

Multi-stage builds, GPU container configuration, Docker Compose for local development, and integration with CI/CD pipelines. MLOps-focused roles often require Kubernetes knowledge on top of Docker.

Can I get a remote Docker job without Kubernetes experience?

Yes — especially at smaller companies or for roles focused on development environments rather than production orchestration. As companies scale, Kubernetes becomes increasingly expected.

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