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Quantum Machine Learning Jobs in Boston, MA (NOW HIRING)

NorthEastern

Sr Research Scientist

Burlington, MA

$107K - $136K/yr

... machine learning, unmanned and autonomous system technologies, as well as quantum materials and sensing. This position is with KRI at Northeastern University, LLC, a wholly-owned subsidiary of NU.

NorthEastern

Research Scientist

Burlington, MA

... machine learning, unmanned and autonomous system technologies, as well as quantum materials and sensing. This position is with KRI at Northeastern University, LLC, a wholly-owned subsidiary of NU.

Northeastern University

Sr Research Scientist

Burlington, MA · On-site

$107K - $136K/yr

... machine learning, unmanned and autonomous system technologies, as well as quantum materials and sensing. This position is with KRI at Northeastern University, LLC, a wholly-owned subsidiary of NU.

NorthEastern

Senior Research Scientist

Burlington, MA

$107K - $136K/yr

... machine learning, unmanned and autonomous system technologies, as well as quantum materials and sensing. This position is with KRI at Northeastern University, LLC, a wholly-owned subsidiary of NU.

Acceler8 Talent

Associate Recruiter

Boston, MA · On-site

$50K/yr

... Machine Learning, Hardware Acceleration, Silicon Photonics and Quantum Computing. We're looking for an ambitious Candidate Consultant to join our growing Boston team. Working alongside experienced ...

New

Acceler8 Talent

Candidate Consultant

Boston, MA · On-site

$50K/yr

... Machine Learning, Hardware Acceleration, Silicon Photonics and Quantum Computing. We're looking for an ambitious Candidate Consultant to join our growing Boston team. Working alongside experienced ...

New

Acceler8 Talent

Graduate Recruitment Consultant

Boston, MA · On-site

$50K/yr

... Machine Learning, Hardware Acceleration, Silicon Photonics and Quantum Computing. We're looking for an ambitious Candidate Consultant to join our growing Boston team. Working alongside experienced ...

New

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All JobsQuantum Machine Learning JobsQuantum Machine Learning Jobs in Boston, MA

Quantum Machine Learning information

See Boston, MA salary details

$27.7K

$46.3K

$95.6K

How much do quantum machine learning jobs pay per year?

As of Sep 3, 2026, the average yearly pay for quantum machine learning in Boston, MA is $46,263.00, according to ZipRecruiter salary data. Most workers in this role earn between $35,300.00 and $50,000.00 per year, depending on experience, location, and employer.

What is a quantum machine learning?

A Quantum Machine Learning (QML) job involves applying principles of quantum computing to machine learning tasks. Professionals in this field develop algorithms that leverage quantum systems to improve computational efficiency and solve complex problems faster than classical methods. Responsibilities often include researching quantum algorithms, implementing quantum circuits, and working with tools like Qiskit or TensorFlow Quantum. These roles are typically found in research labs, tech companies, and startups exploring the intersection of AI and quantum technology. Strong backgrounds in quantum mechanics, linear algebra, and computer science are essential.

What does a quantum machine learning professional do?

Quantum Machine Learning professionals often work on exploratory projects at the intersection of quantum computing and artificial intelligence, such as developing new algorithms that leverage quantum hardware for faster data processing or optimizing classical ML models using quantum techniques. Daily tasks may include designing experiments, simulating quantum systems, analyzing results, and collaborating with physicists and software engineers. The work can range from foundational research to applied development, depending on the organization's focus. These roles frequently involve teamwork and staying updated on emerging academic and industry advances to ensure innovative problem-solving approaches.

What are the key skills and qualifications needed to thrive in quantum machine learning?

To thrive in Quantum Machine Learning, you need a solid background in quantum physics, machine learning, linear algebra, and programming—often supported by a graduate degree in a related field. Familiarity with quantum computing frameworks such as Qiskit or Cirq, and experience with conventional ML libraries like TensorFlow or PyTorch are typically expected. Strong problem-solving abilities, effective communication, and a collaborative mindset help professionals stand out. Mastery of these skills and qualities is essential for tackling complex interdisciplinary challenges and driving innovation in this rapidly evolving field.

Is quantum machine learning a good career?

Quantum machine learning is an emerging field combining quantum computing and machine learning, with growing research and industry interest. Careers in this area typically require strong backgrounds in quantum physics, computer science, and programming skills, often involving specialized tools like quantum algorithms and hardware. As the field develops, demand for experts is expected to increase, making it a promising career path for those with relevant expertise.

What are popular job titles related to Quantum Machine Learning jobs in Boston, MA?

For Quantum Machine Learning jobs in Boston, MA, the most frequently searched job titles are:

What job categories do people searching Quantum Machine Learning jobs in Boston, MA look for?

The top searched job categories for Quantum Machine Learning jobs in Boston, MA are:

Quantum Machine Learning jobs near you
Infographic showing various Quantum Machine Learning job openings in Boston, MA as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 22% Part Time, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $46,263 per year, or $22.2 per hour.

Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA • On-site

Full-time

Re-posted 29 days ago

Key responsibilities

  • Develop and enhance quantitative models for portfolio risk, including factor-based and statistical approaches

  • Analyze large, high-dimensional financial datasets to uncover structure, dependencies, and sources of risk

  • Design and implement analytical tools and pipelines using Python and SQL

Job description

Ph.D. Graduate Intern – Quantitative Portfolio Risk Analytics (Cross-Disciplinary)

Position Overview
We are seeking an exceptional Ph.D. graduate student to join our team as a Quantitative Portfolio Risk Analytics Intern. This role focuses on developing and applying advanced analytical methods to understand portfolio risk, market structure, and complex financial systems.
We are intentionally recruiting from cross-disciplinary, research-driven backgrounds. Doctoral candidates from fields such as physics, astrophysics, math, applied mathematics, statistics, engineering, economics, computer science, quantum computing, biotech, and other data-intensive sciences are strongly encouraged to apply—especially those interested in translating rigorous quantitative methods into real-world financial applications.
Key Responsibilities
  • Develop and enhance quantitative models for portfolio risk, including factor-based and statistical approaches 
  • Analyze large, high-dimensional financial datasets to uncover structure, dependencies, and sources of risk 
  • Design and implement analytical tools and pipelines using Python and SQL 
  • Contribute to model validation, backtesting, and performance evaluation 
  • Collaborate with risk, engineering, and data teams to improve model scalability and data infrastructure 
  • Communicate complex quantitative insights through clear visualizations and technical summaries 
  • Apply advanced methodologies from your discipline (e.g., stochastic modeling, optimization, machine learning, or geometric/topological approaches) to improve risk analytics 
Required Qualifications
  • Currently enrolled in a graduate Ph.D. program in a highly quantitative field (e.g., Math, Applied Mathematics, Physics, Astrophysics, Statistics, Computer Science, Engineering, Financial Engineering, Economics, Biotech or other data-driven disciplines) 
  • Strong foundation in probability, statistics, and numerical methods 
  • Proficiency in Python (NumPy, pandas, or similar) and/or SQL 
  • Experience working with large datasets and implementing quantitative models 
  • Ability to think rigorously about complex systems and translate theory into practical solutions 
Preferred Qualifications
  • Familiarity with quantitative finance concepts (e.g., portfolio theory, factor models, volatility modeling, Value-at-Risk) 
  • Experience with scientific computing, optimization, or machine learning 
  • Background or research in cross-disciplinary areas such as: 
    • Statistical physics, complex systems, or network theory 
    • Applied or computational mathematics 
    • Machine learning or probabilistic modeling 
    • Quantum computing or advanced optimization techniques 
    • Topological data analysis or geometric data methods 
  • Prior research, publications, or project work demonstrating advanced quantitative modeling 
What You’ll Gain
  • Exposure to real-world portfolio risk problems at the intersection of finance and advanced analytics 
  • Opportunity to apply cutting-edge academic methods in a production environment 
  • Collaboration with a highly quantitative, cross-disciplinary team 
  • Experience working with large-scale financial data and modern analytics infrastructure 
  • Mentorship and potential pathway to full-time quantitative roles 
Duration & Compensation
  • Internship: Summer 2026, with potential to extend 
  • Paid internship (competitive, based on experience and location)