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Machine Learning Quantum Computing Jobs (NOW HIRING)

DCS Corp

Quantum Engineer SME

Springfield, VA

$86K - $114K/yr

Deliver technical expertise and analysis across quantum computing technologies, including quantum machine learning, quantum architectures, and quantum simulation. Provide functional analysis, system ...

DCS CORPORATION

Quantum Engineer SME

Springfield, VA · On-site

$86K - $115K/yr

Deliver technical expertise and analysis across quantum computing technologies, including quantum machine learning, quantum architectures, and quantum simulation. Provide functional analysis, system ...

INCA Engineering

Quantum SME

Springfield, VA · On-site

$86K - $114K/yr

Provide technical knowledge and analysis of quantum computing technologies such as quantum machine learning, quantum architectures, and quantum simulation. * Provide functional analysis, design ...

DCS Corporation

Quantum Engineer SME

Springfield, VA

$86K - $115K/yr

Deliver technical expertise and analysis across quantum computing technologies, including quantum machine learning, quantum architectures, and quantum simulation. Provide functional analysis, system ...

Ecole de technologie superieure

$100K/yr

ChampsDescription HTMLIntroduction - titre Professor in quantum computing - MEIE Chair in Quantum ... Then consideration for complementary fields, including : * quantum machine learning; * quantum ...

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All JobsMachine Learning Quantum Computing Jobs

Machine Learning Quantum Computing information

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$25.5K

$42.6K

$88K

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

As of Aug 1, 2026, the average yearly pay for machine learning quantum computing in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 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.
More about Machine Learning Quantum Computing jobs
What cities are hiring for Machine Learning Quantum Computing jobs? Cities with the most Machine Learning Quantum Computing job openings:
What states have the most Machine Learning Quantum Computing jobs? States with the most job openings for Machine Learning Quantum Computing jobs include:
What job categories do people searching Machine Learning Quantum Computing jobs look for? The top searched job categories for Machine Learning Quantum Computing jobs are:
Machine Learning Quantum Computing jobs near you
Infographic showing various Machine Learning Quantum Computing job openings in the United States as of July 2026, with employment types broken down into 83% Full Time, 15% Part Time, and 2% Contract. Highlights an 80% Physical, 3% Hybrid, and 17% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Machine Learning Engineer

Quantum Machines

Santa Barbara, CA • On-site

Full-time

Re-posted 3 days ago

Job description

Description
Quantum Machines (QM) is a global leader in quantum computing control systems. Through our pioneering hardware and software solutions based on instruction-based quantum control, we're revolutionizing how quantum computers are built and controlled. As we stand at the forefront of exponential growth in quantum computing, we're assembling an elite team that actively shapes the evolution of quantum technologies.
We are looking for a Machine Learning Engineer to design, build, and deploy machine learning systems that improve the calibration, control, and operation of quantum processors. In this role, you will work at the intersection of machine learning, quantum physics, and software engineering, translating noisy, non-stationary, safety-critical control problems into ML solutions that run on real hardware in production labs.
You will develop reinforcement learning policies, Bayesian inference methods, and agentic frameworks that make quantum control more autonomous, more sample-efficient, and more robust to drift. This position offers unprecedented exposure to diverse qubit types and quantum architectures, with a tight feedback loop between your models and the systems they steer, and the opportunity to deliver groundbreaking ML-driven solutions to the labs and companies defining the next generation of quantum systems.
Responsibilities:
  • Develop reinforcement learning, Bayesian inference, and probabilistic modelling approaches for parameter tuning, drift tracking, and adaptive measurement, to be deployed on real hardware.
  • Develop real-time parameter steering for calibration during QEC and between circuits.
  • Develop and maintain agentic frameworks for autonomous system control and calibration.
  • Develop and maintain Python-based ML services and libraries that integrate with the wider Quantum Machines control stack, including QUA, Qualibrate, and the OPX1000.
  • Work directly with customers and partner labs to deploy, validate, and iterate on ML solutions in real experimental environments.
  • Collaborate cross-functionally with product, R&D, and hardware teams, contributing to internal libraries, customer-facing SDKs, and training materials.

Requirements
  • PhD/Master in Machine Learning, Physics, Applied Physics, Quantum Information Science, or a related field. 4+ years of relevant experience
  • Strong background in Machine Learning and Deep Learning, with hands-on experience in at least one of: deep learning, reinforcement learning, agentic AI
  • Strong Python proficiency, including scientific or systems-oriented codebases
  • Solid software engineering fundamentals (architecture, Git workflows, testing, code review)
  • Proven track record of taking ML from prototype to deployment under real-world constraints - non-stationary data, expensive evaluations, or safety-critical action spaces. Robotics, online control, autonomous vehicles, or hardware-in-the-loop ML all transfer well
  • Strong problem-solving skills and customer-focused mindset; ability to work independently and in multidisciplinary teams
  • Proven software development track record and excellent technical communication skills
  • Familiarity with quantum computing concepts - qubit calibration, randomized benchmarking, QEC, optimal control- advantage
  • Experience with sim-to-real, multi-objective RL, or meta-learning- advantage

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