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Kubeflow Jobs (NOW HIRING)

IntraEdge

GCP Engineer

Woonsocket, RI · On-site

$54.50 - $72.50/hr

Knowledge of Kubeflow, Vertex AI, or ML pipelines * Experience integrating AI-driven automation into monitoring and incident response Required Qualifications * Strong experience with Kubernetes and ...

Xforia, Inc.

Site Reliability Engineer

Austin, TX · On-site

$140K/yr

Experience with MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc., experience with Docker and Kubernetes * Experience developing containers and Kubernetes in cloud computing ...

IntraEdge

GCP Engineer

Woonsocket, RI · On-site

$54.50 - $72.50/hr

Knowledge of Kubeflow, Vertex AI, or ML pipelines * Experience integrating AI-driven automation into monitoring and incident response Required Qualifications * Strong experience with Kubernetes and ...

Programmers.io

Java with AI ML ENgineer

Dallas, TX · On-site

$51.25 - $70.25/hr

Enable continuous learning and model retraining workflows using Vertex AI or Kubeflow on GCP * Enable observability and reliability of AI decisions by logging model predictions, confidence scores and ...

Texas State Library and Archives Commision

Sr ML Engineer

$107K - $146K/yr

Work on Kubeflow pipelines independently and propose standards. Knowledge of Feature Engineering, Feature Store, and audit capabilities. Expertise in standard software engineering methodology, e.g ...

Redfish Recruiting

Experience with AI/ML flow, Kubeflow, Vertex AI, SageMaker, or similar platforms. * Background in model governance, drift detection, fairness/bias evaluation, and compliance. * Domain specialization ...

Visa

Sr. ML Engineer

Austin, TX · Hybrid

$130K/yr

Kubernetes & Kubeflow Management:Deploy, configure, and manage Kubernetes clusters and Kubeflow to orchestrate complex ML training and deployment pipelines. * Model Deployment & LLMOps:Build robust ...

Visa

Sr. ML Engineer

Austin, TX · On-site

$130K/yr

Kubernetes & Kubeflow Management: Deploy, configure, and manage Kubernetes clusters and Kubeflow to orchestrate complex ML training and deployment pipelines. * Model Deployment & LLMOps: Build robust ...

Hirekeyz Inc

Lead Engineer- Cloud Product

Alpharetta, GA · On-site

$100K - $131K/yr

Experienced with modern ML frameworks (TensorFlow, PyTorch, Hugging Face, etc.) and MLOps tools (Kubeflow, MLflow, Vertex AI Pipelines). * Proven record developing and deploying secure, enterprise ...

Accord Technologies Inc.

MLOPS Engineer

Chicago, IL · On-site

Title: MLOPS Engineer Location: Chicago, IL Duration: 12+ months Position type: W2 contract Required Skills f or the MLOps Engineer: - Bachelor's plus 9+ years of experience ...

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Kubeflow information

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$208K

How much do kubeflow jobs pay per year?

As of Jul 10, 2026, the average yearly pay for kubeflow in the United States is $156,999.00, according to ZipRecruiter salary data. Most workers in this role earn between $136,500.00 and $208,000.00 per year, depending on experience, location, and employer.

What is a Kubeflow job?

A Kubeflow job is a workload running on Kubeflow, typically involving machine learning (ML) tasks such as training, tuning, or batch inference. It leverages Kubernetes resources to efficiently manage and scale ML workflows. Kubeflow provides components like TFJob, PyTorchJob, and MPIJob to support various ML frameworks. These jobs ensure reproducibility, scalability, and portability of ML models in cloud or on-prem environments.

What are the key skills and qualifications needed to thrive in the Kubeflow position, and why are they important?

To thrive as a Kubeflow engineer or specialist, you need a solid background in machine learning operations (MLOps), containerization (especially Kubernetes), and Python programming, often supported by experience with cloud platforms such as AWS, GCP, or Azure. Familiarity with tools like Kubeflow Pipelines, Docker, and CI/CD systems, along with certifications in Kubernetes or cloud technologies, are highly beneficial. Strong problem-solving skills, effective communication, and a collaborative mindset are critical soft skills for this position. These capabilities enable you to efficiently develop, deploy, and scale ML workflows, ensuring robust and seamless machine learning operations in production environments.

What are some common challenges faced by Kubeflow engineers when deploying machine learning models in production?

Kubeflow engineers commonly encounter challenges such as ensuring seamless integration between various ML pipeline components, optimizing resource allocation within Kubernetes clusters, and maintaining reproducibility and scalability of experiments. Navigating the complexities of version control for data, code, and models, as well as monitoring and troubleshooting pipeline failures, also require careful attention. Collaboration with data scientists, DevOps engineers, and stakeholders is essential to address these issues effectively. Overcoming these obstacles helps maintain efficient, reliable, and production-ready machine learning workflows.

More about Kubeflow jobs
What are the most commonly searched types of Kubeflow jobs? The most popular types of Kubeflow jobs are:
What states have the most Kubeflow jobs? States with the most job openings for Kubeflow jobs include:
What job categories do people searching Kubeflow jobs look for? The top searched job categories for Kubeflow jobs are:
Kubeflow jobs near you
Infographic showing various Kubeflow job openings in the United States as of July 2026, with employment types broken down into 50% Full Time, and 50% Contract. Highlights an 50% In-person, and 50% Remote job distribution, with an average salary of $156,999 per year, or $75.5 per hour.
Site Reliability Engineer

Site Reliability Engineer

Donato Technologies, Inc

Austin, TX • On-site

$56.50 - $75/hr

Contractor

Re-posted 17 days ago

Job description

Title: Site Reliability Engineer SRE – ML platform

Location: Austin, TX OR Sunnyvale, CA

Title: Site Reliability Engineer SRE – ML platform 

Responsibilities –

  • Continuous  Deployment using GitHub Actions, Flux, Kustomize
  • Design and implement cloud solutions, build MLOps on cloud AWS
  • Data science model containerization, deployment using docker, VLLM, Kubernetes
  • Communicate with a team of data scientists, data engineers and architects, document the processes
  • Develop and deploy scalable tools and services for our clients to handle machine learning training and inference.
  • Knowledge of ML models and LLM

Qualifications:

  • 6+ years of experience in ML Ops with strong knowledge in Kubernetes, Python, MongoDB and AWS.
  • Good understanding of Apache SOLR.
  • Proficient with Linux administration.
  • Knowledge of ML models and LLM.
  • Ability to understand tools used by data scientists and experience with software development and test automation
  • Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions (AWS)
  • Experience working with cloud computing and database systems
  • Experience building custom integrations between cloud-based systems using APIs
  • Experience developing and maintaining ML systems built with open-source tools
  • Experience with MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc., experience with Docker and Kubernetes
  • Experience developing containers and Kubernetes in cloud computing environments
  • Familiarity with one or more data-oriented workflow orchestration frameworks (Kubeflow, Airflow, Argo, etc.)
  • Ability to translate business needs to technical requirements
  • Strong understanding of software testing, benchmarking, and continuous integration
  • Exposure to machine learning methodology and best practices
  • Good communication skills and ability to work in a team

Note: Focus is to have 60% SRE and 40% ML Ops…