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Machine Learning Quantum Computing Jobs in Texas

CGI

Machine Learning Operations Engineer

Dallas, TX · On-site

$113K - $136K/yr

... machine learning models in production environments. Desired candidate will work closely with ... Deep understanding of the Hadoop ecosystem, distributed computing, and performance tuning.

webAI Inc

Senior Machine Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

We are seeking a Senior Machine Learning Engineer to support our Public Sector initiatives focused ... Strong understanding of GPU computing, CUDA, and performance profiling. We at webAI are committed ...

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Machine Learning Quantum Computing information

What is the difference between Machine Learning Quantum Computing vs Data Scientist?

AspectMachine Learning Quantum ComputingData Scientist
Required CredentialsAdvanced degrees in quantum computing, machine learning, or related fieldsDegree in data science, statistics, or computer science
Work EnvironmentResearch labs, tech companies focusing on quantum tech, academiaBusiness environments, tech companies, consulting firms
Industry UsageEmerging quantum tech industry, research institutionsFinance, healthcare, marketing, e-commerce
Common Search/ComparisonQuantum algorithms, quantum machine learningData analysis, predictive modeling

Machine Learning Quantum Computing specialists focus on developing algorithms that leverage quantum mechanics to enhance machine learning tasks, often requiring advanced knowledge of quantum physics. Data Scientists analyze and interpret large datasets using traditional machine learning techniques. While both roles involve machine learning, the former emphasizes quantum computing applications, whereas the latter centers on data analysis in conventional computing environments.

What are the key skills and qualifications needed to thrive as a Machine Learning Quantum Computing Specialist, and why are they important?

To thrive in Machine Learning Quantum Computing, you need strong foundations in quantum mechanics, linear algebra, and advanced machine learning concepts, typically supported by a degree in physics, computer science, or a related field. Familiarity with quantum programming languages (such as Qiskit or Cirq), cloud-based quantum platforms, and proficiency in Python are usually required, alongside experience with relevant certifications or coursework. Strong problem-solving skills, adaptability, and effective collaboration are vital soft skills in this interdisciplinary field. These competencies are crucial for driving innovation and bridging the gap between quantum computing and practical machine learning applications.

How do professionals in Machine Learning Quantum Computing typically collaborate with interdisciplinary teams?

Professionals in Machine Learning Quantum Computing often work closely with experts in physics, computer science, and engineering. Collaboration usually involves translating quantum concepts for machine learning specialists and vice versa, ensuring that algorithms are both theoretically sound and practically implementable on quantum hardware. Regular meetings, code reviews, and knowledge-sharing sessions are standard, as interdisciplinary insight is crucial for advancing research and developing scalable solutions. Effective communication and a willingness to learn from other domains are essential for success in these teams.

What is Machine Learning Quantum Computing?

Machine Learning Quantum Computing is an interdisciplinary field that combines principles of quantum computing with machine learning techniques. It aims to leverage the computational power of quantum computers to enhance the performance of machine learning algorithms, potentially solving complex problems more efficiently than classical computers. This area includes developing quantum algorithms for tasks such as classification, clustering, and optimization, as well as using machine learning to improve quantum hardware and error correction. Researchers expect that, as quantum hardware matures, this field could revolutionize data analysis, cryptography, and scientific discovery.
What are popular job titles related to Machine Learning Quantum Computing jobs in Texas? For Machine Learning Quantum Computing jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Machine Learning Quantum Computing jobs in Texas look for? The top searched job categories for Machine Learning Quantum Computing jobs in Texas are:
What cities in Texas are hiring for Machine Learning Quantum Computing jobs? Cities in Texas with the most Machine Learning Quantum Computing job openings:
Machine Learning Quantum Computing jobs near you
Software Engineer - Machine Learning - IV

Software Engineer - Machine Learning - IV

Judge Group, Inc.

Richardson, TX • On-site

$88K - $121K/yr

Other

Re-posted 10 days ago

Job description

Location: Richardson, TX Salary: Depends on Experience Description: Our client is currently seeking a Software Engineer - Machine Learning - IV
This job will have the following responsibilities:
  • We are seeking a highly skilled AI Systems Contractor to join our advanced AI engineering team. This role is ideal for someone with deep technical expertise in Large Language Models (LLMs), agentic AI workflows, and high-performance computing environments. You will play a critical role in designing, building, and optimizing AI solutions that leverage NVIDIA GPUs within a Red Hat OpenShift platform.
    You'll work across the full AI development lifecycle-from rapid prototyping in Jupyter Notebooks to deploying scalable, production-grade systems.
    Responsibilities
    • Develop & Fine-Tune AI Models: Build and optimize LLMs and other deep learning models tailored to project needs.
    • Design Agentic AI Systems: Architect and implement multi-agent AI workflows capable of autonomous task execution.
    • GPU Optimization on OpenShift: Deploy and manage AI/ML workloads on OpenShift, ensuring efficient GPU utilization.
    • Python & Jupyter Development: Use Python and Jupyter for prototyping, experimentation, and model training.
    • Pipeline & Infrastructure Management: Maintain robust data/model pipelines and troubleshoot performance bottlenecks in containerized environments.
    • Cross-Functional Collaboration: Partner with data scientists, engineers, and project managers to deliver high-impact AI solutions.

    Minimum Qualifications
    • Proven experience developing AI systems, including LLMs and agentic AI architectures.
    • Advanced proficiency in Python and experience with frameworks like PyTorch, TensorFlow, LangChain, and Hugging Face.
    • Hands-on expertise with Jupyter Notebooks for model development.
    • Direct experience deploying containerized AI workloads using NVIDIA GPUs on Red Hat OpenShift.
    • Strong understanding of Docker, Kubernetes, and CI/CD practices.
    • Excellent problem-solving skills in high-performance computing environments.
    • Bachelor's degree in Computer Science, Engineering, or a related field-or equivalent practical experience.

    Preferred Qualifications
    • Experience with distributed training and model serving at scale.
    • Familiarity with MLOps tools and practices.
    • Contributions to open-source AI projects or research publications.

By providing your phone number, you consent to: (1) receive automated text messages and calls from the Judge Group, Inc. and its affiliates (collectively "Judge") to such phone number regarding job opportunities, your job application, and for other related purposes. Message & data rates apply and message frequency may vary. Consistent with Judge's Privacy Policy, information obtained from your consent will not be shared with third parties for marketing/promotional purposes. Reply STOP to opt out of receiving telephone calls and text messages from Judge and HELP for help.
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This job and many more are available through The Judge Group. Please apply with us today!

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