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Ibm Quantum Machine Learning Jobs (NOW HIRING)

DCS CORPORATION

Quantum Engineer SME

Springfield, VA · On-site

$86K - $115K/yr

Apply principles and methods from quantum machine learning and related quantum computing fields to address highly complex, narrowly scoped technical challenges in engineering and other scientific ...

DCS Corp

Quantum Engineer SME

Springfield, VA

$86K - $114K/yr

Apply principles and methods from quantum machine learning and related quantum computing fields to address highly complex, narrowly scoped technical challenges in engineering and other scientific ...

DCS Corporation

Quantum Engineer SME

Springfield, VA · On-site

$86K - $115K/yr

Apply principles and methods from quantum machine learning and related quantum computing fields to address highly complex, narrowly scoped technical challenges in engineering and other scientific ...

The Pennsylvania State University

Quantum Machine Learning and AI: Develop novel quantum algorithms and computational frameworks for machine learning, generative AI, data analytics, and intelligent decision-making, while advancing ...

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Ibm Quantum Machine Learning information

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$25.5K

$42.6K

$88K

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

As of Aug 6, 2026, the average yearly pay for ibm quantum machine learning in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What is IBM Quantum Machine Learning?

IBM Quantum Machine Learning refers to the use of quantum computing technologies, developed by IBM, to solve machine learning problems. It involves leveraging quantum algorithms and quantum hardware to process and analyze data in ways that are often infeasible for classical computers. This field explores how quantum computers can offer speed-ups or new capabilities in areas such as classification, clustering, and optimization. IBM provides tools like Qiskit Machine Learning to help researchers and developers experiment with quantum-enhanced machine learning models. The aim is to unlock new possibilities in artificial intelligence by integrating quantum computing into the machine learning workflow.

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

AspectIbm Quantum Machine LearningData Scientist
Required CredentialsBackground in quantum computing, machine learning, and programming languages like PythonDegree in Data Science, Statistics, Computer Science, or related fields
Work EnvironmentResearch labs, tech companies, or academic institutions focusing on quantum algorithms and AIBusiness environments, tech firms, or consulting agencies analyzing data for insights
Industry UsageEmerging field combining quantum computing and machine learning applicationsWidely used across industries for data analysis, predictive modeling, and decision-making

While both roles involve machine learning, Ibm Quantum Machine Learning focuses on developing algorithms that leverage quantum computing, requiring specialized knowledge in quantum mechanics and programming. Data Scientists analyze large datasets using classical computing tools. The roles differ mainly in technical focus and tools used, but both are integral to advancing AI and data-driven solutions.

What are the key skills and qualifications needed to thrive as an IBM Quantum Machine Learning specialist?

To thrive as an IBM Quantum Machine Learning Specialist, you need a strong background in quantum computing, machine learning algorithms, and programming languages like Python, often supported by a degree in computer science, physics, or related fields. Familiarity with IBM Quantum Experience, Qiskit, and cloud-based quantum computing platforms is typically required, along with knowledge of classical ML frameworks such as TensorFlow or PyTorch. Strong problem-solving skills, collaboration, and the ability to communicate complex concepts clearly are standout soft skills in this role. These skills are crucial for developing, optimizing, and deploying quantum-enhanced machine learning solutions in interdisciplinary teams and advancing cutting-edge research.

What are some common challenges faced when working in IBM Quantum Machine Learning roles, and how can they be addressed?

Professionals in IBM Quantum Machine Learning often encounter challenges such as limited access to advanced quantum hardware, the steep learning curve associated with quantum algorithms, and the need to bridge classical and quantum computing concepts. To address these, team members frequently collaborate with experts in both quantum physics and machine learning, leverage cloud-based quantum systems like IBM Quantum Experience, and participate in ongoing training. Adapting to rapid technological advancements and staying updated with the latest research are also essential for success in this evolving field.
What are the most commonly searched types of Ibm Quantum Machine Learning jobs? The most popular types of Ibm Quantum Machine Learning jobs are:
What job categories do people searching Ibm Quantum Machine Learning jobs look for? The top searched job categories for Ibm Quantum Machine Learning jobs are:
Infographic showing various Ibm Quantum Machine Learning job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 24% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Engineer, Machine Learning

Beyond SOF

Washington, DC • On-site

Full-time

Re-posted 1 hour ago

Job description

Role Summary:
The Machine Learning Engineer is
responsible for developing and
implementing machine learning models
and algorithms to solve complex
problems.
Main Responsibilities and Duties:
Develop and implement machine
learning models and algorithms.
Collaborate with the engineering team to
integrate machine learning solutions into
projects.
Stay updated on the latest machine
learning technologies and trends.
Develop and implement quantum
machine learning models and
algorithms. Collaborate with quantum
engineers to integrate quantum
machine learning solutions into the
company's projects.

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