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Remote Kubeflow Jobs (NOW HIRING)

Zscaler

AI DevOps Engineer

$54 - $74/hr

Experience with MLOps frameworks: kubeflow, ML flow, and model services like Bedrock, Sagemaker, Vertex AI, Gemini Enterprise #LI-Remote #LI-YC2

Berriehill Research

Senior AI Systems Engineer

Raleigh, NC · On-site +1

$92K - $126K/yr

... such as MLflow, Kubeflow, vLLM, or similar. * Experience with simulations for scientific or ... This position may be performed fully remote, hybrid, or onsite at an ARA office. Preference will be ...

Mondo

Data Scientist

Phoenix, AZ · Remote

$65 - $75/hr

Remote (Candidate must reside in Pacific, Mountain, or Central time zone. Eastern time zone and ... Direct experience with at least one MLOps tooling stack such as MLflow, Kubeflow, SageMaker ...

Azumo

Data Scientist, Applied AI - Remote

The position is FULLY REMOTE , based in Latin America. Professional English proficiency (B2/C1 ... Exposure to MLOps/LLMOps tools ( MLflow , Kubeflow , TFX ). * Experience with Large Language Models ...

Moody's Analytics

MLOps Engineer

New York, NY · On-site +1

We have a flexible work environment and allow remote work depending on one's personal choice ... Familiarity with MLflow (or similar platforms like Kubeflow and other tools) * Promotes a practice ...

Nebius

Key Customers Solutions Architect

$64.50 - $85/hr

Hands-on experience with HPC/ML orchestration frameworks (e.g., Slurm, Kubeflow). * Experience with ... Remote Work Reimbursement: Up to $85/month for mobile and internet. * Disability & Life Insurance:

Nebius

Hands-on experience with HPC/ML orchestration frameworks (e.g., Slurm, Kubeflow). * Experience with ... Remote Work Reimbursement: Up to $85/month for mobile and internet. * Disability & Life Insurance:

OpenTeams

Senior Engineering Architect

OR · On-site +1

$145K - $250K/yr

S - Remote Work Authorization: U.S. citizenship required Clearance: An active clearance is not ... Ray, Kubeflow, MCP, and open-weight large language models * Identify, document, and mitigate ...

Nebius

Solutions Architect

$64.50 - $85/hr

Slurm, Kubeflow) * Hands-on experience with deep learning frameworks (e.g. TensorFlow, PyTorch ... Remote Work Reimbursement: Up to $85/month for mobile and internet. * Disability & Life Insurance:

SAIC

AI Machine Learning Developer Principal

Chantilly, VA · On-site +1

$160K - $200K/yr

None Potential for Remote Work: ORA_HYBRID Description We are seeking to build a team of AI/ML ... Familiarity with MLOps and orchestration platforms (e.g., MLflow, Kubeflow, Apache Airflow, Triton ...

New

Pavago

Full-Stack AI Engineer

Remote (LATAM, Eastern Europe, Pakistan, India, South Africa Preferred) About the Role We are ... as MLflow, Kubeflow, Vertex AI, or SageMaker • Knowledge of microservices, serverless ...

SAIC

AI Machine Learning Developer Principal

Arlington, VA · On-site +1

$160K - $200K/yr

None Potential for Remote Work: ORA_HYBRID Description We are seeking to build a team of AI/ML ... Familiarity with MLOps and orchestration platforms (e.g., MLflow, Kubeflow, Apache Airflow, Triton ...

New

Affirm

Machine Learning Engineer II (Underwriting ML)

$146K - $206K/yr

... e.g., Kubeflow, Airflow, MLflow, or equivalent internal platforms). - Proficient in using AI ... The majority of our roles are remote and you can work almost anywhere within the country of ...

Airbnb

Senior Machine Learning Engineer, Payments

$107K - $146K/yr

... platforms (Kubeflow, Airflow), largescale data streaming & processing (Spark, Ray, Kafka ... This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or ...

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Remote Kubeflow information

What is a remote Kubeflow?

A Remote Kubeflow job refers to a role where professionals use Kubeflow, an open-source machine learning platform designed for Kubernetes, while working remotely. These jobs typically involve designing, deploying, and managing machine learning workflows on cloud or on-premises Kubernetes clusters. Responsibilities may include automating ML pipelines, optimizing model training, and collaborating with data scientists and engineers. Remote Kubeflow professionals usually need expertise in Kubernetes, Docker, Python, and machine learning concepts. The remote aspect allows them to perform these tasks from anywhere with reliable internet access.

What are the key skills and qualifications needed to thrive as a remote Kubeflow engineer?

To thrive as a Remote Kubeflow Engineer, you need strong expertise in machine learning, cloud computing, and container orchestration, typically supported by a degree in computer science or related fields. Proficiency with tools such as Kubeflow, Kubernetes, Docker, and cloud platforms like AWS, GCP, or Azure—as well as experience with CI/CD pipelines—is essential. Strong problem-solving skills, communication, and the ability to collaborate remotely are important soft skills for success. These skills ensure the effective deployment and management of scalable machine learning workflows in distributed, cloud-based environments.

What are some common challenges faced by professionals working in a remote Kubeflow engineer role?

Remote Kubeflow engineers often encounter challenges such as troubleshooting distributed machine learning pipelines without direct, on-premises access to infrastructure. Effective communication with data scientists, DevOps, and other stakeholders can also be more complex due to differing time zones and remote collaboration tools. Additionally, managing secure access and ensuring seamless deployment of ML workflows in cloud environments requires a strong understanding of both Kubernetes and Kubeflow. Overcoming these challenges typically involves proactive documentation, regular virtual meetings, and a collaborative approach to problem-solving.

What is the difference between Remote Kubeflow vs Remote Data Scientist?

AspectRemote KubeflowRemote Data Scientist
Required CredentialsCloud certifications, Kubernetes, ML OpsStatistics, Machine Learning, Programming
Work EnvironmentCloud platforms, DevOps toolsData analysis, modeling, research
Industry UsageAI/ML deployment, MLOps teamsData analysis, predictive modeling

Remote Kubeflow focuses on deploying and managing ML workflows using Kubernetes, requiring cloud and DevOps skills. Remote Data Scientists analyze data, build models, and interpret results. While both roles involve machine learning, Remote Kubeflow emphasizes deployment and infrastructure, whereas Remote Data Scientists focus on data analysis and modeling.

More about Remote Kubeflow jobs

What cities are hiring for Remote Kubeflow jobs?

Cities with the most Remote Kubeflow job openings:

What are the most commonly searched types of Kubeflow jobs?

The most popular types of Kubeflow jobs are:

What states have the most Remote Kubeflow jobs?

States with the most job openings for Remote Kubeflow jobs include:

What job categories do people searching Remote Kubeflow jobs look for?

The top searched job categories for Remote Kubeflow jobs are:

Remote Kubeflow jobs near you
Infographic showing various Remote Kubeflow job openings in the United States as of August 2026, with employment types broken down into 94% Full Time, 1% Temporary, and 5% Contract. Highlights an 75% Physical, 5% Hybrid, and 20% Remote job distribution.

AI DevOps Engineer

Zscaler

Remote

$54 - $74/hr

Full-time

Posted 18 days ago

Job description

Role 

We are looking for an AI DevOps Engineer to join our team. This is a Remote within the United States (with a hybrid preference for San Jose, CA) role, reporting to the Manager, IT Cloud Operations in the Cloud Platform Engineering department. Our team builds and operates the internal cloud platform that powers Zscaler's product and corporate infrastructure across AWS, GCP, and Azure. In this role, you will design and ship automation that provisions cloud environments, enforces security baselines, and integrates AI-assisted tooling and agentic workflows to accelerate delivery.

What you'll do (Role Expectations)

  • Build and extend Day 1 automation, including infrastructure provisioning pipelines, account vending, golden repo templates, and CI/CD components
  • Build and extend Day 2 automation for lifecycle management, upgrade pipelines, dependency scanning, drift detection, and automated remediation workflows
  • Write and maintain Terraform modules, GitLab CI/CD components, and Python automation to establish the platform's paved road
  • Integrate AI and agentic tooling into operational workflows to reduce manual toil and increase operational consistency
  • Collaborate with Security, IAM, Network, and FinOps teams to translate cross-functional requirements into automated guardrails

Who You Are (Success Profile)

  • You thrive in ambiguity. You are comfortable building the path as you walk it, viewing dynamic environments as raw material to build something meaningful.
  • You act like an owner. Your passion for the mission fuels your bias for action, seamlessly navigating between high-level strategy and hands-on execution.
  • You are a problem-solver. You seek out challenges because you are energized by finding solutions, knowing that solving hard problems delivers maximum impact.
  • You are a high-trust collaborator. You embrace a challenge culture by giving and receiving ongoing feedback with clarity, respect, and candor.
  • You are a learner. You bring a true growth mindset and actively seek feedback to continuously develop yourself and support your team.

What We're Looking for (Minimum Qualifications)

  • Demonstrated curiosity and active exploration of AI tools, with a proven history of integrating new technologies to enhance daily workflows and augment problem-solving
  • 5+ years of experience building and operating multi-cloud infrastructure at scale across major cloud platforms
  • Hands-on expertise with Terraform, including module design, state management, and CI-driven workflows
  • Proficiency in scripting and automation using Python, Bash, or equivalent languages
  • Working knowledge of CI/CD pipeline design and Kubernetes operations
  • Understanding of identity federation, secrets management, and least-privilege security patterns

What Will Make You Stand Out (Preferred Qualifications)

  • Experience with multi-account governance tooling such as AWS Control Tower, Organizations, SCPs, or RCPs
  • Experience with GitOps patterns and automated infrastructure lifecycle tooling such as Renovate, Dependabot, or ArgoCD
  • Experience with MLOps frameworks: kubeflow, ML flow, and model services like Bedrock, Sagemaker, Vertex AI, Gemini Enterprise

#LI-Remote #LI-YC2

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