ZipRecruiter

Log In

1

Machine Learning Quantum Computing Jobs in Boston, MA

NorthEastern

Postdoctoral Research Associate

Boston, MA

$60K - $85K/yr

... machine learning, or quantum computing are big pluses. The candidate should have good communication ... skills, both oral and written, and are willing to work in a multidisciplinary environment. Position ...

NorthEastern

Postdoctoral Research Associate

Boston, MA · On-site

$60K - $85K/yr

... machine learning, or quantum computing; experience in two or more areas is a strong plus * Strong oral and written communication skills with a willingness to contribute to a multidisciplinary ...

NorthEastern

Postdoctoral Research Associate-1

Boston, MA · On-site

$60K - $85K/yr

... machine learning, or quantum computing; experience in two or more areas is a strong plus * Strong oral and written communication skills with a willingness to contribute to a multidisciplinary ...

Queracomputinginc

Sr Technical Product Manager

Boston, MA · On-site

$170K - $250K/yr

Boston, MA Reports to: VP of Quantum Computing Services Summary QuEra is seeking a Senior Technical ... Year 1 Focus Stand up and drive the next-generation machine program across 3 threads: * [Strategic ...

New

next page

Showing results 1-20

All JobsMachine Learning Quantum Computing JobsMachine Learning Quantum Computing Jobs in Boston, MA

Machine Learning Quantum Computing information

See Boston, MA salary details

$27.7K

$46.3K

$95.6K

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

As of Jul 11, 2026, the average yearly pay for machine learning quantum computing 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 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 are popular job titles related to Machine Learning Quantum Computing jobs in Boston, MA? For Machine Learning Quantum Computing jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Machine Learning Quantum Computing jobs in Boston, MA look for? The top searched job categories for Machine Learning Quantum Computing jobs in Boston, MA are:
What cities near Boston, MA are hiring for Machine Learning Quantum Computing jobs? Cities near Boston, MA with the most Machine Learning Quantum Computing job openings:
Machine Learning Quantum Computing jobs near you
AI for Quantum Operations Lead

AI for Quantum Operations Lead

QuEra Computing, Inc.

Boston, MA • On-site

Full-time

Re-posted 2 days ago

Job description

Role Summary
The AI for Quantum Operations Lead owns the roadmap and execution strategy for AI-assisted calibration, diagnostics, prediction, and recovery across quantum systems, ensuring that AI improves machine uptime, calibration speed, and operator decision-making while deterministic control and safety software remain authoritative.
Key Responsibilities
  • Define and drive the AI operations roadmap across calibration optimization, atom image/readout analysis, drift prediction, root-cause diagnosis, and recovery recommendation.
  • Partner with quantum systems, controls, software, hardware, and ML teams to identify high-value workflows where AI can safely propose, rank, predict, or optimize.
  • Establish the bounded-AI operating model: AI provides recommendations or constrained optimizations, while deterministic control software enforces timing, hardware limits, validation, rollback, and safety logic.
  • Prioritize AI pilots for Quokka, Calibration Manager, telemetry systems, readout pipelines, and QPU operations workflows.
  • Own requirements for dataset traceability, model validation, observability, offline replay, deployment gates, and operator-facing explainability.
  • Translate machine-performance pain points into measurable AI/ML objectives such as reduced calibration time, fewer failed jobs, faster recovery, improved readout quality, and better drift detection.
  • Coordinate cross-functional execution, staffing needs, milestones, risk reviews, and stakeholder communication.

Required Background
  • Strong technical leadership experience in AI/ML, controls, robotics, scientific instrumentation, or complex hardware operations.
  • Experience bringing ML models into production environments where reliability, safety, traceability, and human/operator trust matter.
  • Ability to work across software, hardware, physics, and operations teams.
  • Strong systems thinking; understands where AI should help, where deterministic software must remain in charge, and how to design the boundary between them.

Preferred Background
  • Experience with Bayesian optimization, active learning, time-series forecasting, computer vision, anomaly detection, or root-cause analysis.
  • Familiarity with calibration workflows, lab automation, telemetry systems, or hardware-in-the-loop validation.
  • Exposure to quantum computing, neutral atoms, optical systems, embedded control, or real-time systems.

Success Measures
  • Clear AI operations roadmap with owners, milestones, and safety gates.
  • First bounded AI pilots deployed into calibration or readout workflows.
  • Measurable reduction in calibration effort, diagnosis time, drift-related failures, or recovery time.
  • Strong governance around model validation, data provenance, deployment approval, and operator trust.

QuEra is committed to cultivating a diverse work environment and is proud to be an equal opportunity employer. We highly value diversity in our current and future employees and do not discriminate (including in our hiring and promotion practices) based on race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law.

Apply