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Kubeflow Jobs (NOW HIRING)

InstantServe LLC

Data Scientist

Design, train, and deploy ML/AI models using MLOps frameworks (MLflow, Kubeflow, CI/CD) * Develop and implement GenAI solutions (RAG, prompt engineering, fine-tuning, agentic workflows) * Apply ...

Hirekeyz Inc

MLOps Engineer

San Francisco, CA · On-site

Develop and maintain ML pipelines using tools like MLflow, Kubeflow, or Vertex AI. * Automate model training, testing, deployment, and monitoring in cloud environments (e.g., GCP, AWS, Azure)

Sage IT Inc

SRE with MLops Platform

Sunnyvale, CA · On-site

$67 - $89/hr

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

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How much do kubeflow jobs pay per year?

As of Jul 9, 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.
Data Scientist MLOps (MLflow, Kubeflow, Airflow)

Data Scientist MLOps (MLflow, Kubeflow, Airflow)

TekCommands Inc

Philadelphia, PA • On-site

Contractor

Re-posted 23 days ago

Job description

-Required Skills & Qualifications
• 5+ years of experience in Data Science, Machine Learning, or related fields.
• Strong expertise in Python, SQL, and modern ML frameworks (TensorFlow, PyTorch, Scikit-Learn).
• Experience with MLOps tools (MLflow, Kubeflow, Airflow) for model deployment and monitoring.
• Proficiency in cloud platforms (GCP) and scalable data engineering.
• Experience implementing and testing recommendation engines.
• Experience with Knowledge Graphs and their integration into AI/ML pipelines.
• Strong understanding of probability theory, statistics, and experimental design (A/B Testing).
• Experience with collaborative software engineering practices (Agile, DevOps).
• Bachelor's or Master’s degree in Computer Science, Mathematics, Engineering, or related field.
Preferred Qualifications
• Background in Retail and Personalization Web Technologies.
• Hands-on experience in LLMs (e.g., GPT, BERT, LLaMA, Claude) and Generative AI technologies.
• Understanding of client’s digital ecosystem and data-driven decision-making.
• Proficiency in business intelligence (BI) tools and data visualization.