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Quantum Machine Learning Engineer Jobs (NOW HIRING)

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Machine Learning Engineer Position: Full time Location: Carlsbad office About Us: NTENT provides a Platform-as-a-Service (PaaS), allowing industry partners to customize, localize and integrate search ...

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Machine Learning Engineer Position: Full time Location: Carlsbad office About Us: NTENT provides a Platform-as-a-Service (PaaS), allowing industry partners to customize, localize and integrate search ...

Eccalon LLC

Machine Learning Engineer

Detroit, MI · On-site

Machine Learning Engineer Location: Detroit, MI- Onsite Type: Full-time Security Clearance: No clearance required, must be clearable. The Machine Learning Engineer will be an essential member of the ...

Orchard LLC

Machine Learning Engineer

Machine Learning Engineer Remote with occasional travel to Silver Spring, MD About @Orchard: @Orchard is a growing Woman-Owned Small Business and federal prime contractor delivering mission-critical ...

3B Staffing LLC

Machine Learning Engineer

Tahoma, CA · On-site

Machine Learning Engineer (Molecular Design) Location: Hybrid - Bay Area, CA We are seeking a Machine Learning Engineer with expertise in molecular design to support cutting-edge drug discovery ...

Booz Allen Hamilton

Machine Learning Engineer

Aurora, CO · On-site

$78 - $176/hr

As a programmer, you know that machine learning is critical to understanding and processing massive datasets. Your ability to conduct statistical analyses on business processes using ML techniques ...

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Quantum Machine Learning Engineer information

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$31.5K

$128.8K

$193.5K

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

As of Sep 5, 2026, the average yearly pay for quantum machine learning engineer in the United States is $128,769.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,500.00 and $155,000.00 per year, depending on experience, location, and employer.

What is a quantum machine learning engineer?

A Quantum Machine Learning Engineer is a professional who combines expertise in quantum computing and machine learning to develop algorithms and solutions that leverage quantum hardware for advanced data processing tasks. They work on designing, implementing, and testing quantum algorithms that can solve problems faster or more efficiently than classical computers. Their work often involves collaborating with physicists, data scientists, and software engineers to bridge the gap between quantum theory and practical applications. This role requires strong backgrounds in quantum mechanics, computer science, and statistical learning techniques.

What are the key skills and qualifications needed to thrive as a quantum machine learning engineer?

To thrive as a Quantum Machine Learning Engineer, you need a strong background in quantum computing, machine learning, linear algebra, and programming (often Python or C++), typically supported by an advanced degree in physics, computer science, or a related field. Familiarity with platforms like Qiskit, Cirq, or TensorFlow Quantum, and knowledge of quantum algorithms and cloud-based quantum computing services are essential. Creative problem-solving, analytical thinking, and strong collaboration skills help distinguish top performers in this interdisciplinary field. Mastery of these skills enables innovation in developing and deploying quantum machine learning solutions to solve complex, cutting-edge problems.

How do quantum machine learning engineers typically collaborate with classical machine learning teams and quantum hardware specialists?

Quantum Machine Learning Engineers often serve as a bridge between classical machine learning experts and quantum hardware specialists. They work closely with data scientists to adapt machine learning algorithms for quantum environments and collaborate with hardware teams to ensure algorithms are optimized for specific quantum processors. Regular cross-functional meetings, code reviews, and joint problem-solving sessions are common, fostering a highly collaborative work environment. This collaboration is essential for successfully integrating quantum solutions into existing workflows and advancing the organization's quantum computing initiatives.

Is quantum machine learning a good career?

Quantum machine learning engineers work at the intersection of quantum computing and machine learning, focusing on developing algorithms that leverage quantum hardware. The field is emerging with high growth potential, requiring skills in quantum algorithms, programming languages like Python, and understanding of both quantum mechanics and machine learning principles. As quantum technology advances, demand for specialists in this area is expected to increase, making it a promising career path for those with relevant expertise.
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Quantum Machine Learning Engineer jobs near you
Infographic showing various Quantum Machine Learning Engineer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 24% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $128,769 per year, or $61.9 per hour.

Machine Learning Engineer

5 Star Global Recruitment Partners

Newark, NJ

Full-time

Posted 4 days ago

Job description

Machine Learning Engineer

Newark, New Jersey, United States

Job Description

As a Machine Learning Engineer, you will play a pivotal role in driving the development and implementation of cutting-edge machine learning solutions for our client. Your responsibilities will encompass a wide range of tasks, from leading a small team of machine learning engineers to collaborating with cross-functional teams to deliver impactful solutions. You will be at the forefront of driving innovation and leveraging the power of machine learning to solve real-world problems, drive business growth, and create value.

Key Responsibilities

  • Lead and drive machine learning projects from inception to production: build relationships with business partners and cross-functional teams.
  • Collaborate with business leaders, subject matter experts, and decision-makers to develop success criteria and optimize new products, features, policies, and models.
  • Partner with data scientists to understand, implement, train, and design machine learning models.
  • Collaborate with the infrastructure team to improve the architecture, scalability, stability, and performance of ML platform.
  • Construct optimized data pipelines to feed machine learning models.
  • Extend existing machine learning libraries and frameworks.
  • Develop processes, model monitoring, and governance framework for successful ML model operationalization.
  • Define objectives for the Machine Learning platform, own the technical roadmap, and be accountable for delivering results.
  • Define standards for engineering and operational excellence for running best-in-class ML platforms and continue to improve ML platforms to keep up with the latest innovations.
  • Design and implement the best architectural practices in the delivery of data science use cases.

Key Skills/Knowledge/Experience

  • 7+ years of experience in Machine Learning.
  • Extensive software engineering experience with strong working experience as a Machine Learning Engineer.
  • Bachelor's degree in computer science, computer engineering, or a related engineering field. Masters degree preferred.
  • Advanced proficiency with Python, Java, and Scala.
  • Strong computer science fundamentals such as algorithms, data structures, multithreading.
  • Experience working with Generative AI, using LangChain for Gen AI and techniques like RAG.
  • Experience using ML and DL Libraries:XGBoost, SKlearn, Tensorflow or PyTorch
  • In-depth experience building solutions using public clouds such as AWS, GCP.
  • Experience using ML platforms like SageMaker, H2O, DataRobot, etc.
  • Strong knowledge on ML model development life cycle components like containers, batch vs real time inference endpoints, application security testing etc.
  • Experience managing relationships in a cross-functional environment with multiple stakeholders.
  • Experience with developing and deploying production-grade applications with ML inferences using automation pipeline on cloud.
  • Experience working in Agile/ Scrum development process.
  • Thought leadership and innovative thinking.
  • Excellent communication and collaboration skills.

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