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Quantum Machine Learning Engineer Jobs in Hingham, MA

Bespoke Labs

Machine Learning Engineer

Brockton, MA · 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

Cambridge, MA · 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 ...

Red Hat, Inc.

Machine Learning Engineer

Boston, MA · On-site

$136.32 - $225.09/hr

## Machine Learning EngineerApplyremote type: Hybridlocations: Bostontime type: Full timeposted on ... Apply standard software engineering practices, including version control, code reviews, and ...

Xometry

Staff Machine Learning Engineer

Waltham, MA · On-site

Xometry is looking for a Staff Machine Learning Engineer to join our growing AI/ML team. This is a senior individual contributor role with broad technical scope and meaningful organizational impact.

Xometry

Staff Machine Learning Engineer

Waltham, MA · On-site

Xometry is looking for a Staff Machine Learning Engineer to join our growing AI/ML team. This is a senior individual contributor role with broad technical scope and meaningful organizational impact.

Capital One

Lead Machine Learning Engineer

Cambridge, MA

$112K - $147K/yr

Lead Machine Learning Engineer At Capital One, we are changing banking for good by creating responsible and reliable AI-powered systems. Our investments in technology infrastructure and world-class ...

Capital One

Lead Machine Learning Engineer

Cambridge, MA · On-site

$112K - $147K/yr

Lead Machine Learning Engineer At Capital One, we are changing banking for good by creating responsible and reliable AI-powered systems. Our investments in technology infrastructure and world-class ...

Motional

Machine Learning Engineer, Data Mining

Boston, MA · On-site +1

$144K - $192K/yr

As a Machine Learning Engineer on the Data Mining team, your mission is to help build the "Brain" of this engine. You will work with state-of-the-art foundation models to extract insights from ...

Motional

Machine Learning Engineer, Data Mining

Boston, MA · On-site +1

$144K - $192K/yr

As a Machine Learning Engineer on the Data Mining team, your mission is to help build the "Brain" of this engine. You will work with state-of-the-art foundation models to extract insights from ...

VideaHealth

Senior Machine Learning Engineer

Boston, MA · On-site

$170K - $205K/yr

About the position: We're looking for a Senior Machine Learning Engineer with deep expertise in some area of ML engineering to join our growing ML team and work closely with our software and computer ...

DraftKings

Senior Machine Learning Engineer

Boston, MA · On-site

$133K - $175K/yr

The Crown Is Yours As a Senior Machine Learning Engineer, you'll design, implement, and scale production-grade data and machine learning pipelines that drive measurable business outcomes. You'll ...

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All JobsQuantum Machine Learning Engineer JobsQuantum Machine Learning Engineer Jobs in Hingham, MA

Quantum Machine Learning Engineer information

See Hingham, MA salary details

$33K

$135K

$202.8K

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

As of Aug 12, 2026, the average yearly pay for quantum machine learning engineer in Hingham, MA is $134,963.00, according to ZipRecruiter salary data. Most workers in this role earn between $106,400.00 and $162,500.00 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.

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?

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 job categories do people searching Quantum Machine Learning Engineer jobs in Hingham, MA look for? The top searched job categories for Quantum Machine Learning Engineer jobs in Hingham, MA are:
What cities near Hingham, MA are hiring for Quantum Machine Learning Engineer jobs? Cities near Hingham, MA 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 Hingham, MA as of June 2026, with employment types broken down into 84% Full Time, 13% Part Time, and 3% Contract. Highlights an 72% Physical, 4% Hybrid, and 24% Remote job distribution, with an average salary of $134,963 per year, or $64.9 per hour.

Principal Systems Engineer - Quantum Computing Systems

QuEra Computing, Inc.

Boston, MA • On-site

Full-time

Re-posted 5 days ago

Job description

Summary
We are seeking a Principal Systems Engineer to play a critical role in aligning engineering execution with scientific and machine-level progress in the development of large-scale quantum computers.
This role sits at the intersection of quantum science, hardware engineering, and control software, with a primary mission to make the system coherent, buildable, and integrable as it evolves. Unlike traditional product environments, many system requirements in quantum computing are discovered through experimentation, not defined upfront. Success in this role requires deep collaboration with scientists, rapid learning, and the ability to introduce structure only where it accelerates progress.
The ideal candidate brings extensive experience building complex hardware-software systems (e.g., aerospace, EVs, robotics, advanced instrumentation) and is motivated to apply systems engineering rigor in a learning-driven R&D environment, pairing closely with internal quantum experts.
This is a technical leadership role with broad influence across teams. Authority comes from clarity, usefulness, and trust - not from gatekeeping or heavy process.
Core Mission
  • Bridge the gap between engineering tasks and quantum machine milestones
  • Make system architecture, integration status, and technical risk visible and actionable
  • Enable scientists and engineers to move faster together by reducing ambiguity, friction, and rework
  • Help the organization evolve from ad-hoc integration to disciplined, scalable system development - without slowing discovery

Responsibilities
1. System Understanding Through Scientific Partnership
  • Work closely and continuously with quantum scientists to understand how the machine is actually operated, tuned, and debugged in practice.
  • Spend significant time in the lab, observing experiments and participating in scientific discussions to absorb tacit system knowledge.
  • Treat scientists as primary system knowledge holders, approaching requirement gathering as a learning and synthesis exercise.
  • Build trust by accurately reflecting scientific intent and constraints in system models, requirements, and architectural decisions.

2. Requirements Co-Evolution & Traceability
  • Facilitate the co-evolution of system requirements as the machine progresses:
    • Start with lightweight, provisional requirements
    • Explicitly document uncertainty, assumptions, and open questions
    • Refine requirements as experimental results and understanding improve
  • Translate scientific goals (e.g., performance, stability, operability) into actionable engineering requirements while preserving necessary flexibility.
  • Establish traceability between:
    • machine-level goals
    • subsystem requirements
    • engineering deliverables (e.g., JIRA epics)
  • Ensure engineers understand the intent behind requirements, not just the wording.

3. System Architecture & Integration Leadership
  • Develop and maintain a living system architecture covering:
    • quantum hardware
    • control electronics and firmware
    • control software and orchestration layers
  • Produce clear, accessible architecture diagrams that reflect reality and evolve with the system.
  • Identify missing architectural elements, poorly defined interfaces, and integration risks early.
  • Lead system-level trade studies and technical decision-making in partnership with engineering and scientific leaders.
  • Ensure architecture reflects machine milestones and not just organizational boundaries.

4. Integration, Test Strategy, and Machine Protection
  • Define and drive a system integration and test strategy appropriate for an evolving R&D machine.
  • Help ensure engineering testbeds match machine configurations as closely as possible.
  • Push integration testing upstream so that machines are not used as primary test platforms.
  • Partner with engineering teams to define validation criteria tied to real machine behavior.
  • Reduce burden on lab teams by improving pre-deployment testing and integration readiness.

5. Early Wins & Trust Building
  • Deliver small, tangible improvements early that directly reduce friction for scientists and engineers, such as:
    • clarifying a recurring interface problem
    • creating a simple integration checklist
    • documenting a failure mode that saves days of debugging
  • Use these wins to establish credibility and reinforce the value of systems engineering.
  • Continuously gather feedback on what is helping vs. slowing teams down, and adapt approach accordingly.

6. Unlearning, Adaptation, and Process Design
  • Actively identify where traditional systems engineering assumptions do not apply to quantum computing.
  • Unlearn rigid models around fixed requirements, early design freezes, and linear development.
  • Introduce process and structure incrementally, calibrated to system maturity.
  • Champion systems thinking without dogma - prioritizing outcomes over formality.

7. Technical Leadership & Mentorship
  • Serve as a technical leader and mentor for engineers and emerging systems thinkers.
  • Help scale system knowledge through documentation, diagrams, and internal education.
  • Contribute to leadership discussions on technical strategy, integration risk, and execution pacing.

Qualifications
  • 10+ years of experience in systems engineering or system architecture for complex hardware-software systems.
  • Proven experience integrating multidisciplinary systems (hardware, software, controls).
  • Strong background in requirements definition, interface management, and system integration.
  • Ability to operate effectively in ambiguous, fast-evolving R&D environments.
  • Exceptional communication and collaboration skills.

Preferred Qualifications
  • Experience in aerospace, automotive, robotics, advanced instrumentation, or similar domains.
  • Familiarity with model-based systems engineering (MBSE) concepts or tools.
  • Experience working closely with research or experimental teams.
  • Exposure to quantum technologies is a plus, but not required.

Personal Attributes We Value
  • Intellectual humility and curiosity
  • Comfort learning from domain experts
  • Bias toward clarity and usefulness
  • Strong listening skills
  • Pragmatic, outcome-driven mindset

Why This Role Matters
You will help build a machine that has never existed before - not by enforcing process, but by making the system understandable, integrable, and scalable. This role is foundational to turning scientific breakthroughs into reliable quantum computers.
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.
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