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Remote Kubeflow Jobs in Texas (NOW HIRING)

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.

What are the most commonly searched types of Kubeflow jobs in Texas?

The most popular types of Kubeflow jobs in Texas are:

What are popular job titles related to Remote Kubeflow jobs in Texas?

For Remote Kubeflow jobs in Texas, the most frequently searched job titles are:

What cities in Texas are hiring for Remote Kubeflow jobs?

Cities in Texas with the most Remote Kubeflow job openings:

Infographic showing various Remote Kubeflow job openings in Texas as of September 2026, with employment types broken down into 89% Full Time, 2% Temporary, and 9% Contract. Highlights an 72% Physical, 4% Hybrid, and 24% Remote job distribution.

A/AI Research Engineer Stf - E4

Fort Worth, TX • On-site, Remote

Lockheed Martin Corporation
Manufacturing • 10K+ employees

$150K - $280K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 6 days ago


Lockheed Martin rating

8.2

Company rating: 8.2 out of 10

Based on 398 frontline employees who took The Breakroom Quiz

38th of 72 rated aerospace companies


Job description

Standard Job Description Focuses on research & development of technologies that enable and advance semi and fully autonomous systems for both defense and commercial customers. Serves as the algorithm expert with up-to-date knowledge on modern AI research and may be involved in the inception of ideas and drive the development cycles from research to test of prototypes for a major project or component of a major project.Researches and discovers improvements to machine learning and robotic algorithms; Drives advancements in techniques used in signal processing, computer vision (CV), and control systems; Develops AI algorithms for mission systems; Applies latest research on AI algorithms and trains machine learning / deep learning models to solve a variety of problems; Investigates and applies the latest machine learning and deep learning techniques; Optimizes the performance of AI algorithms, applications, and platforms; Develops and documents algorithm and implementation requirements; Develops prototypes that will enable autonomous functionality in LM products and platforms; Interfaces with other teams involved the development lifecycle for perception, mission and motion planning, simulation and modeling, testing, etc. The Astris AI Sales Engineer serves as the technical backbone of our AI Factory go-to-market motion

This role owns the demo environments, proof-of-concept architectures, and technical narrative that show prospective enterprise customers what AI Factory platform - can do. It combines hands-on AI/ML engineering skill with strong presentation ability to support Account Executives across the full commercial sales cycle, from first technical discovery call through proof-of-concept and close. Demo & Technical Asset Development Build and maintain reusable MLOps demo environments on Panel covering the full model lifecycle: experiment tracking, versioning, CI/CD for ML, deployment, and production monitoring Develop industry-specific demo narratives and datasets (predictive maintenance, fraud detection, supply chain forecasting, and similar) that map Panel's capabilities to a prospect's actual workflows Maintain demo infrastructure, including containerized and Kubernetes-based environments, so demos run reliably across customer meetings, trade shows, and remote sessions Build reusable technical assets: reference architectures, ROI calculators, solution briefs, and competitive comparison sheets Customer Engagement Support Partner with Account Executives throughout the commercial sales cycle - qualification through close Lead technical discovery sessions to understand a prospect's existing infrastructure, data environment, team structure, and integration constraints Deliver customized demonstrations and technical presentations to audiences ranging from data scientists and ML engineers to CTO/CIO-level executives Respond to RFIs/RFPs with accurate technical content and MLOps-specific competitive positioning Build trusted-advisor relationships with customer technical stakeholders and support technical handoffs to Customer Success Engineers and Solution Architects once a deal closes Technical Enablement & Collaboration Maintain deep expertise in Panel and general MLOps best practices (model versioning, experiment tracking, CI/CD for ML, monitoring, governance) Stay current on the broader MLOps and Kubernetes ecosystem (Kubeflow, MLflow, KServe, Ray, Argo, and similar) to keep demos and competitive positioning sharp Collaborate with Product and Engineering to feed customer feedback and market signal into the Panel roadmap Support partner-channel enablement (ISVs, SIs, cloud partners) with MLOps-focused technical training and co-selling assets Basic Qualifications 5-9 years in Sales Engineering, Solutions Engineering, Pre-Sales Technical Consulting, or a hands-on ML engineering / MLOps role Working proficiency in Python and at least one ML framework (PyTorch, TensorFlow, scikit-learn, or similar) Demonstrated ability to build and maintain demo or POC environments end-to-end Desired Skills Hands-on experience with MLOps platforms such as Astris AI Factory, MLflow, Kubeflow, Weights & Biases, or similar tools Working knowledge of generative AI / LLM concepts (RAG, agent frameworks) Experience selling into manufacturing, supply chain, financial services, or technology verticals Practical experience with Kubernetes (deploying, debugging, or operating workloads) and containerization Pay Information GeoZone Definition: GeoZones are geographic groupings created by Lockheed Martin to align compensation ranges with regional labor markets and cost-of-labor differences across the United States.

Locations are assigned a Geo Zone based on the primary work location of the role. Full-time salary range (GEOZONE 1): $150800.00 - $280000.00 Includes metropolitan areas such as Sunnyvale CA; Pal Alto, CA; New York City metropolitan area; Newark, New Jersey; etc. Full-time salary range (GEOZONE 2): $135700.00 - $251900.00 Includes metropolitan areas such as Denver, CO; King of Prussia, PA; Stratford, CT; Moorestown, NJ; etc

Full-time salary range (GEOZONE 3): $120600.00 - $224000.00 Includes metropolitan areas such as Dallas-Fort Worth, TX; Orlando, FL; Grand Prairie, TX; Marietta, GA; etc. Full-time salary range (GEOZONE 4): $108600.00 - $201600.00 Includes metropolitan areas such as Camden, AR; Lexington, KY; Ocala, FL; Lufkin, TX; etc. At Lockheed Martin, we know mission success starts with taking care of our people

Our Total Rewards program is designed to attract top talent, support your well-being, and help you grow-both professionally and personally. The salary range for this position is as listed on the requisition. Please note that the salary information listed is a general guideline only.

Lockheed Martin considers factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience, education/ training, key skills as well as market(work location) and business considerations when extending an offer. Benefits offered: Medical, Dental, Vision, Flexible work arrangements and schedules (e.g., 4x10), 401(k) match, Paid time off, Holidays, Parental Leave, EAP, Flexible Spending Accounts, Education Assistance, Life Insurance, Short-Term Disability, and Long-Term Disability. Annual short-term and/or long-term incentive compensation programs may be offered depending on the position

Payments under these annual programs are not guaranteed and can vary from year to year and are tied to a range of performance metrics. For (Washington state applicants only) Non-represented full-time employees: accrue at least 10 hours per month of Paid Time Off (PTO) to be used for incidental absences and other reasons; receive at least 90 hours for holidays. Represented full time employees accrue 6.67 hours of Vacation per month; accrue up to 52 hours of sick leave annually; receive at least 96 hours for holidays

PTO, Vacation, sick leave, and holiday hours are prorated based on start date during the calendar year.


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About Lockheed Martin

Sourced by ZipRecruiter

As a global security and aerospace company, the majority of Lockheed Martin's business is with the U.S. Department of Defense and U.S. federal government agencies.The remaining portion of Lockheed Martin's business is comprised of international government and commercial sales of products, services and platforms.

Industry

Manufacturing

Company size

10,000+ Employees

Headquarters location

Bethesda, MD, US

Year founded

1912