ZipRecruiter

Log In

1

Quantum Machine Learning Engineer Jobs in Minnesota

Bespoke Labs

Machine Learning Engineer

Plymouth, MN · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Duluth, MN · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Rochester, MN · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

AeroVironment

Machine Learning Engineer II

Minneapolis, MN · On-site

$102K - $144K/yr

Worker Type Regular Summary The Machine Learning Engineer II will be a member of the Learning and Active Perception (LEAP) group in AV's MacCready Works division and develop a variety of innovative ...

Shipt

Staff Machine Learning Engineer

Minneapolis, MN · On-site

Impact As a Staff Machine Learning Engineer on Shipt's Personalization Platform team you will drive key AI initiatives. In this role, you'll collaborate with Data Scientists to design and deploy ...

next page

Showing results 1-20

All JobsQuantum Machine Learning Engineer JobsQuantum Machine Learning Engineer Jobs in Minnesota

Quantum Machine Learning Engineer information

See Minnesota salary details

$30.9K

$126.1K

$189.5K

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

As of Jun 26, 2026, the average yearly pay for quantum machine learning engineer in Minnesota is $126,118.00, according to ZipRecruiter salary data. Most workers in this role earn between $99,400.00 and $151,800.00 per year, depending on experience, location, and employer.

What is the salary of quantum machine learning engineer?

The salary of a quantum machine learning engineer typically ranges from $100,000 to $150,000 annually, depending on experience, education, and location. Senior roles or those with specialized skills in quantum algorithms and programming languages like Python or Qiskit may offer higher compensation. Entry-level positions generally start around $80,000 to $100,000.

What engineers make $500,000?

Senior engineers in specialized fields such as software engineering, data engineering, or engineering management can earn $500,000 or more annually, especially with extensive experience, advanced skills, and in high-demand industries like technology or finance. These roles often require advanced degrees, certifications, and leadership responsibilities.

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.

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.

Which 3 jobs will survive AI?

Quantum Machine Learning Engineers are likely to have resilient careers as their role combines advanced quantum computing and machine learning skills, which are less susceptible to automation. Jobs requiring complex problem-solving, creativity, and specialized expertise—such as data scientists, AI researchers, and cybersecurity analysts—are also expected to persist. These roles often involve tasks that are difficult for AI to fully automate and require ongoing human oversight and innovation.

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.

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

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.
What are popular job titles related to Quantum Machine Learning Engineer jobs in Minnesota? For Quantum Machine Learning Engineer jobs in Minnesota, the most frequently searched job titles are:
What job categories do people searching Quantum Machine Learning Engineer jobs in Minnesota look for? The top searched job categories for Quantum Machine Learning Engineer jobs in Minnesota are:
What cities in Minnesota are hiring for Quantum Machine Learning Engineer jobs? Cities in Minnesota with the most Quantum Machine Learning Engineer job openings:
Quantum Machine Learning Engineer jobs near you
Infographic showing various Quantum Machine Learning Engineer job openings in Minnesota as of June 2026, with employment types broken down into 89% Full Time, 9% Part Time, and 2% Contract. Highlights an 72% Physical, 3% Hybrid, and 25% Remote job distribution, with an average salary of $126,118 per year, or $60.6 per hour.
Machine Learning Engineer

Machine Learning Engineer

Javen Technologies, Inc

New Ulm, MN • On-site

Other

Posted 2 days ago

Job description

Position: Machine Learning Engineer

Location: New Ulm, MN

Duration: 6+ Months (Contract to Hire)

Job Description:

The Impact You ll Make in this Role

As an ML Engineer, you will be responsible for building and maintaining the pipelines that power AI in our Healthcare Information Systems (HIS). We are looking for a practical, detail-oriented engineer who is passionate about MLOps, data reliability, and production stability.

In this role, you won t just be building models; you will be ensuring those models work reliably in the real world. You will help bridge the gap between data science and software engineering by implementing automated workflows, managing cloud infrastructure, and ensuring our AI services are secure and scalable.

Key Responsibilities:

MLOps & Deployment

  • Pipeline Development: Build and maintain CI/CD pipelines for machine learning, focusing on automated testing, model deployment, and version control (using tools like MLflow or Git).
  • Model Serving: Deploy ML models as scalable APIs and microservices, ensuring they meet performance and latency requirements for clinical use.
  • Monitoring: Implement basic monitoring tools to track model performance, data drift, and system health in production.

Data Engineering & Integration

  • Data Pipelines: Develop and optimize ETL processes to transform healthcare data (FHIR, HL7) into clean, usable datasets for model training and inference.
  • Feature Management: Help build and maintain feature stores and data layers that ensure consistency between training and production environments.
  • System Integration: Work closely with backend teams to integrate ML outputs into our core healthcare applications.

Engineering Best Practices

  • Code Quality: Write clean, maintainable, and well-documented Python code. Participate in code reviews to ensure system reliability.
  • Containerization: Use Docker and Kubernetes to package and orchestrate ML workloads across different environments.
  • Security & Compliance: Follow established protocols to ensure all data handling and deployments meet HIPAA and HITRUST security standards

Skills and Expertise:

To set you up for success in this role from day one, Solventum requires (at a minimum) the following qualifications:

  • Bachelor s or Master s degree in Computer Science, Software Engineering, Data Engineering, or a related field.
  • 3 5 years of professional experience in software engineering or data engineering, with at least 2 years focused on machine learning production environments.

AND

  • Programming: Strong proficiency in Python and familiarity with SQL. Knowledge of a compiled language (like Go or Java) is a plus.
  • Cloud & Infrastructure: Hands-on experience with at least one major cloud provider (AWS, Azure, or Google Cloud Platform) and containerization (Docker).
  • ML Tools: Familiarity with ML libraries (PyTorch or Scikit-learn) and MLOps tools (like Airflow, Prefect, BentoML, or Kubeflow).
  • Data Tools: Experience with data processing frameworks (like Pandas, Spark, or dbt).

Education:

Additional qualifications that could help you succeed even further in this role include:

  • Familiarity with deploying Large Language Models (LLMs) or using frameworks like LangChain.
  • Experience working in a regulated environment (Healthcare, Finance, etc.).
  • Understanding of API design and microservices architecture.

Apply