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Machine Learning Quantum Computing Jobs in Saint Louis, MO

Varsity Tutors

Linear Algebra Tutor

Saint Louis, MO · Remote

$18 - $40/hr

... machine learning, and quantum mechanics applications. * Curriculum Awareness & Adaptive Instruction ... vector spaces, computing determinants of large matrices, and grasping the significance of ...

Koantek

Senior AI Engineer

Chesterfield, MO · On-site

$54.75 - $70.50/hr

... computing frameworks, specifically in scalable machine learning and high-performance data processing (e.g., using technologies like Apache Spark). • Contribute to the strategic growth of the ML ...

Koantek

Senior AI Engineer

Chesterfield, MO · Remote

$54.75 - $70.50/hr

Leverage expertise in distributed computing frameworks, specifically in scalable machine learning and high-performance data processing (e.g., using technologies like Apache Spark). Contribute to the ...

Varsity Tutors

Python Tutor

Saint Louis, MO · Remote

$18 - $40/hr

Emphasizes readable, maintainable code and connects Python to machine learning, web scraping, scientific computing, and DevOps applications. * Curriculum Awareness & Adaptive Instruction: Familiar ...

TekWissen LLC

IT Data Engineer (Entry)

Creve Coeur, MO · On-site

$58/hr

... machine learning solutions. What You Will Do: * Write robust, well-documented code * Engage with ... Experience with virtual machines and cloud computing * Interdisciplinary communication and ...

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All JobsMachine Learning Quantum Computing JobsMachine Learning Quantum Computing Jobs in Saint Louis, MO

Machine Learning Quantum Computing information

See Saint Louis, MO salary details

$24.8K

$41.4K

$85.6K

How much do machine learning quantum computing jobs pay per year?

As of Jul 14, 2026, the average yearly pay for machine learning quantum computing in Saint Louis, MO is $41,401.00, according to ZipRecruiter salary data. Most workers in this role earn between $31,600.00 and $44,700.00 per year, depending on experience, location, and employer.

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 job categories do people searching Machine Learning Quantum Computing jobs in Saint Louis, MO look for? The top searched job categories for Machine Learning Quantum Computing jobs in Saint Louis, MO are:
Machine Learning Quantum Computing jobs near you
Machine Learning Engineer with Security Clearance

Machine Learning Engineer with Security Clearance

SecureVision

Saint Louis, MO • On-site

Other

Re-posted 4 days ago

Job description

HOW A MACHINE LEARNING ENGINEER WILL MAKE AN IMPACT
Own your opportunity to serve as a critical component of our nation's safety and security. Make an impact by using your expertise to protect our country from threats. Job Description
Rapidly prototype containerized multimodal deep learning solutions and associated data pipelines to enable GeoAI capabilities for improving analytic workflows and addressing key intelligence questions. You will be at the cutting edge of implementing State-of-the-Art (SOTA) Computer Vision (CV) and Vision Language Models (VLM) for conducting image retrieval, segmentation tasks, AI-assisted labeling, object detection, and visual question answering using geospatial datasets such as satellite and aerial imagery, full-motion video (FMV), ground photos, and OpenStreetMap.
WHAT YOU'LL NEED TO SUCCEED:
• Education: Bachelor or Master' Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or equivalent experience in lieu of degree.
• Experience: 5+ years Technical skills:
• Demonstrated experience applying transfer learning and knowledge distillation methodologies to fine-tune pre-trained foundation and computer vision models to quickly perform segmentation and object detection tasks with limited training data using satellite imagery.
• Demonstrated professional or academic experience building secure containerized Python applications to include hardening, scanning, automating builds using CI/CD pipelines.
• Demonstrated professional or academic experience using Python to query and retrieve imagery from S3 compliant API's perform common image preprocessing such as chipping, augment, or conversion using common libraries like Boto3 and NumPy.
• Demonstrated professional or academic experience with deep learning frameworks such as PyTorch or Tensorflow to optimize convolutional neural networks (CNN) such as ResNet or U-Net for object detection or segmentation tasks using satellite imagery.
• Demonstrated professional or academic experience with version control systems such as Gitlab.
• Demonstrated experience leveraging CUDA for GPU accelerated computing. Skills and abilities desired:
• Demonstrated professional or academic experience with the HuggingFace Transformers library and hub.
• Demonstrated experience with OpenShift and container orchestration within Kubernetes using Helm, Kubectl, Kustomize, or Operators.
• Demonstrated experience with Vision Transformers (ViT) such as DINO or DeiT.
• Demonstrated academic or professional experience communicating methodological choices and model results.
• Demonstrated experience with verification and validation test benches.
• Demonstrated experience with Explainable AI (XAI) techniques.
• Demonstrated experience with Open Neural Net Exchange (ONNX).

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