Docker AI Jobs

Discover the latest remote and onsite Docker AI roles across top active AI companies. Updated hourly.

Check out 252 new Docker AI roles opportunities posted on The Homebase

Senior Software Engineering Director, Developer Experience

New
Top rated
Crusoe
Full-time
Full-time
Posted

As the Senior Director of Engineering for Developer Experience at Crusoe, you will own and drive the strategy, execution, and culture of the team responsible for how Crusoe's engineers and non-engineers build, ship, and operate software. Responsibilities include defining and executing the long-term vision for Crusoe's internal developer platform, which encompasses shared services, internal APIs, repositories, and self-service infrastructure to enable engineering teams to move quickly and confidently. You will also rapidly develop and productionize AI-powered tools for the entire company, creating and evangelizing best practices for productionizing AI-developed tools and evaluating SaaS purchases. Additionally, you will oversee the design, reliability, and continuous improvement of CI/CD pipelines, build systems, and deployment infrastructure to ensure safe and rapid scaling of engineering teams' shipping processes. Your role will also involve defining and driving organization-wide engineering productivity initiatives by establishing metrics, identifying bottlenecks, and implementing tooling and process improvements that enhance developer experience across Crusoe. People leadership is a key responsibility, including managing and growing a team of engineers and fostering a high-performance culture based on accountability, innovation, and continuous learning. Furthermore, you will collaborate with senior leaders across Engineering, Infrastructure, Security, and Product to align Developer Experience investments with company-wide engineering goals and priorities.

$301,750 – $355,000
Undisclosed
YEAR

(USD)

San Francisco, United States
Maybe global
Onsite
Python
JavaScript
CI/CD
Docker
Kubernetes

Software Engineering Manager, Autonomous

New
Top rated
Magical
Full-time
Full-time
Posted

As the Engineering Manager on the Autonomous team, you will lead and scale a high-calibre team of engineers dedicated to defining the future of AI agent development and advancing AI and backend systems. You will oversee the technical roadmap for the Autonomous team, translating architectural complexity into clear product strategies. You will mentor a diverse group of engineers, supporting their professional growth. You will partner closely with Product and Design to ensure the agent-building tools remain intuitive while supporting technical capabilities. You will champion a 'show > tell' culture by ensuring rapid shipping with a high standard for technical stability and user experience. You will clear technical and operational roadblocks to ensure the team operates with high agency and clarity.

Undisclosed

()

San Francisco, United States
Maybe global
Hybrid
Python
Kubernetes
Docker
AWS
CI/CD

Software Engineering Manager, Autonomous

New
Top rated
Magical
Full-time
Full-time
Posted

As an Engineering Manager on the Autonomous team, you will lead and scale a high-calibre team of engineers dedicated to defining the future of AI agent development and advancing AI and backend systems. You will oversee the technical roadmap for the team by translating architectural complexity into clear product strategies, mentor and support the professional growth of a diverse group of engineers, and partner closely with Product and Design to ensure the agent-building tools remain intuitive and technically robust. You will champion a "show > tell" culture to ensure rapid shipping while maintaining high technical stability and user experience standards, and clear technical and operational roadblocks to enable the team to operate with high agency and clarity. You will act as the bridge between product vision and technical execution.

Undisclosed

()

Toronto, Canada
Maybe global
Hybrid
Python
Docker
Kubernetes
AWS
CI/CD

Senior Engineering Manager, Handshake AI

New
Top rated
Handshake
Full-time
Full-time
Posted

The Senior Engineering Manager leads a core product and platform engineering team building systems that integrate human expertise into AI development workflows. This team owns critical infrastructure connecting talent networks, data operations, and research needs into scalable, reliable, and high-quality platforms. The manager leads, hires, and develops a high-performing engineering team, owns the roadmap and execution in partnership with Product, Research, and Operations, drives architecture and technical strategy for scalable and extensible systems, builds modular platforms for new domains, workflows, and partners, raises engineering quality across reliability, observability, performance, and data integrity, and fosters a culture of ownership, velocity, and strong engineering fundamentals in a fast-moving environment.

$230,000 – $300,000
Undisclosed
YEAR

(USD)

San Francisco, United States
Maybe global
Onsite
Python
Docker
Kubernetes
AWS
MLOps

Lead AI/ML Engineer

New
Top rated
ASAPP
Full-time
Full-time
Posted

Lead the design and implementation of scalable ML/AI systems focused on large language models, vector databases, and retrieval-based architectures. Integrate and apply foundation models from providers like OpenAI, AWS Bedrock, and Anthropic for prototyping and production use cases. Adapt, evaluate, and optimize large language models for domain-specific enterprise applications. Build and maintain infrastructure for AI model experimentation, deployment, and monitoring in production. Improve model performance and inference workflows addressing latency, cost, and reliability. Provide technical leadership by mentoring engineers and promoting best ML engineering practices. Partner with product and cross-functional stakeholders to translate requirements into scalable ML solutions. Contribute to the evolution of internal standards for AI experimentation, evaluation, and deployment. Lead the design and delivery of end-to-end voice AI solutions combining large language models with speech technologies including speech-to-text, text-to-speech, and real-time streaming audio pipelines, architecting low-latency, highly reliable conversational voice systems and guiding a team through ambiguity toward production excellence. Understand and apply constraints of voice experiences such as latency, turn-taking, interruption handling, streaming inference, and audio quality to create scalable, enterprise-grade systems.

$170,000 – $190,000
Undisclosed
YEAR

(USD)

New York or Mountain View, United States
Maybe global
Hybrid
Python
PyTorch
TensorFlow
OpenAI API
RAG

Senior Product Designer, Mobile

New
Top rated
Grammarly
Full-time
Full-time
Posted

Own the observability and lifecycle management of AI features across the organization. Build tools and infrastructure to enable teams to develop, monitor, and optimize LLM-powered features. Design and implement closed-loop evaluation pipelines that automatically validate prompt changes. Develop comprehensive metrics and dashboards to track LLM usage including cost per feature, token patterns, and latency. Create systems that tie user feedback to specific prompts and LLM calls. Establish best practices and processes for the full lifecycle of prompts including development, testing, deployment, and monitoring. Collaborate with engineering teams to ensure they have the tools and visibility needed to build high-quality AI features.

$103,000 – $128,000
Undisclosed
YEAR

(USD)

North America
Maybe global
Remote
Go
Kubernetes
Google Cloud
OpenAI API
Prompt Engineering

Head of Product, AI

New
Top rated
Bjak
Full-time
Full-time
Posted

Own the end-to-end AI product strategy grounded in technical feasibility and real-world constraints, translate model capabilities, data limitations, and evaluation results into clear product decisions, make trade-offs across quality, latency, cost, reliability, and user experience, work daily with ML, backend, and mobile engineers on design, evaluation, and iteration, define success metrics and feedback loops across offline evaluation, online experiments, and human feedback, drive execution with clear specifications, risk awareness, and disciplined prioritization, ensure AI features ship quickly, safely, and reliably into production, and own AI product quality across UX, correctness, and outcomes.

Undisclosed

()

Jakarta, Indonesia
Maybe global
Remote
Python
MLflow
Model Evaluation
Prompt Engineering
MLOps

Head of Product, AI

New
Top rated
Bjak
Full-time
Full-time
Posted

Own the end-to-end AI product strategy grounded in technical feasibility and real-world constraints; translate model capabilities, data limitations, and evaluation results into clear product decisions; make hard trade-offs across quality, latency, cost, reliability, and user experience; work daily with ML, backend, and mobile engineers on design, evaluation, and iteration; define success metrics and feedback loops across offline evaluation, online experiments, and human feedback; drive execution with clear specifications, risk awareness, and disciplined prioritization; ensure AI features ship quickly, safely, and reliably into production; and own AI product quality across user experience, correctness, and outcomes.

Undisclosed

()

Beijing, China
Maybe global
Remote
Python
MLflow
Prompt Engineering
OpenAI API
Transformers

Product Manager, Models

New
Top rated
Heidi Health
Full-time
Full-time
Posted

As the Product Manager for Heidi's models platform, you will own the product strategy and roadmap for the platform including evaluation pipelines, fine-tuning infrastructure, model routing, and safety systems. Your responsibilities include prioritising your team's work across enablement requests, model safety and quality, and new capability bets; fixing platform issues that cause blocks for product teams; building evaluation tooling and fine-tuning workflows usable in clinical settings; deciding improvements based on clinician feedback, model quality signals, and product team needs; allocating engineering capacity among competing requests and clearly communicating deferrals; working with engineers on evaluation design, fine-tuning trade-offs, and model architecture decisions; setting model quality and safety targets based on clinical outcomes; consolidating duplicate infrastructure across product teams; and monitoring foundation model developments to adjust the roadmap accordingly. You will collaborate closely with engineers, researchers, product PMs, and clinical safety teams and report to product leadership. This is a platform role whose outputs impact every user-facing product at Heidi.

Undisclosed

()

Sydney, Australia
Maybe global
Remote
Python
Model Evaluation
MLOps
MLflow
Docker

Forward Deployed Engineer, Agentic Platform

New
Top rated
Cohere
Full-time
Full-time
Posted

Build and ship features for North, an AI workspace platform; develop autonomous agents that interact with sensitive enterprise data; experiment rapidly and with high quality to engage customers and deliver solutions that exceed expectations; work across the entire product lifecycle from conceptualization through production; lead end-to-end deployment of North in private cloud and on-premises environments including planning, configuration, testing, and rollout.

Undisclosed

()

Middle East
Maybe global
Onsite
Python
RAG
Docker
Kubernetes
AWS

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[{"question":"What are Docker AI jobs?","answer":"Docker AI jobs involve developing, deploying, and maintaining AI applications using containerization technology. These positions focus on creating reproducible AI workflows, packaging machine learning models with dependencies, and ensuring consistent execution across environments. Professionals in these roles typically work on MLOps pipelines, containerized AI applications, and implement solutions that seamlessly transition from development to production."},{"question":"What roles commonly require Docker skills?","answer":"Machine Learning Engineers, Data Scientists, AI Developers, and DevOps Engineers working on AI systems commonly require containerization skills. These professionals use containers to package models, ensure reproducibility, and streamline deployment pipelines. Full-stack developers building AI-powered applications and MLOps specialists implementing continuous integration workflows also frequently need proficiency with containerized environments and deployment strategies."},{"question":"What skills are typically required alongside Docker?","answer":"Alongside containerization expertise, employers typically seek proficiency in AI frameworks like TensorFlow, PyTorch, and Hugging Face. Familiarity with Docker Compose for multi-container applications, version control systems, and CI/CD pipelines is essential. Additional valuable skills include YAML configuration, cloud deployment knowledge, GPU acceleration techniques, and experience with MLOps practices that facilitate model development, testing, and production deployment."},{"question":"What experience level do Docker AI jobs usually require?","answer":"AI positions requiring containerization skills typically seek mid-level professionals with 2-4 years of practical experience. Entry-level roles may accept candidates with demonstrated proficiency in basic container commands, Dockerfile creation, and image management. Senior positions often demand extensive experience integrating containers into production ML pipelines, optimizing container resources, and implementing advanced deployment strategies across cloud and edge environments."},{"question":"What is the salary range for Docker AI jobs?","answer":"Compensation for AI professionals with containerization expertise varies based on location, experience level, industry, and additional technical skills. Junior roles typically start at competitive market rates, while senior positions command premium salaries. The most lucrative opportunities combine deep learning expertise, container orchestration experience, and cloud platform knowledge. Specialized industries like finance or healthcare often offer higher compensation for these in-demand skill combinations."},{"question":"Are Docker AI jobs in demand?","answer":"Containerization skills remain highly sought after in AI development, with strong demand driven by organizations implementing MLOps practices and scalable AI deployment strategies. Recent partnerships like Anaconda-Docker and trends in serverless AI containers have intensified hiring needs. The emergence of specialized tools like Docker Model Runner, Docker Offload, and Docker AI Catalog reflects the growing importance of containerized workflows in modern AI development and deployment practices."},{"question":"What is the difference between Docker and Kubernetes in AI roles?","answer":"In AI roles, containerization focuses on packaging individual applications with dependencies for consistent execution, while Kubernetes orchestrates multiple containers at scale. ML engineers might use Docker to create reproducible model environments but implement Kubernetes to manage production deployments across clusters. While containerization handles the model packaging, Kubernetes addresses the scalability, load balancing, and automated recovery needed for production AI systems serving multiple users simultaneously."}]