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Quantum Machine Learning Jobs in California (NOW HIRING)

Qualcomm

Machine Learning Researcher

San Diego, CA · On-site

... quantum machine learning). • Develops and publishes research findings in the form of presentations and conference papers. • Conducts research on machine learning and develops innovative or ...

Qualcomm

Machine Learning Researcher

San Diego, CA · On-site

$159K - $238K/yr

... quantum machine learning). • Develops and publishes research findings in the form of presentations and conference papers. • Conducts research on machine learning and develops innovative or ...

Visa

Staff Research Scientist

Foster City, CA · On-site

$169K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

The ideal candidate will focus on conducting advanced research in one or more subfields, for example, quantum error correction, quantum optimization, quantum simulation, quantum machine learning ...

LLNL

Machine Learning Physics Graduate Student

Livermore, CA

$6.7K - $8.2K/mo

  • Retirement

... quantum simulations. * Explore the use of machine learning methods to discover and evolve PDEs for ... phase field and phase field crystal models. * Analyze results, provide weekly updates and present ...

LLNL

Machine Learning Physics Graduate Student

Livermore, CA · On-site

$9.8K/wk

  • Retirement

... quantum simulations. * Explore the use of machine learning methods to discover and evolve PDEs for ... phase field and phase field crystal models. * Analyze results, provide weekly updates and present ...

LLNL

Machine Learning Physics Graduate Student

Livermore, CA · On-site

$6.7K - $8.2K/mo

  • Retirement

... quantum simulations. * Explore the use of machine learning methods to discover and evolve PDEs for ... phase field and phase field crystal models. * Analyze results, provide weekly updates and present ...

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Quantum Machine Learning information

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$25.2K

$42K

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How much do quantum machine learning jobs pay per year?

As of Aug 17, 2026, the average yearly pay for quantum machine learning in California is $42,026.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,100.00 and $45,400.00 per year, depending on experience, location, and employer.

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 technology advances, demand for experts in quantum algorithms and data analysis is expected to increase, making it a promising but highly specialized career path.

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.

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 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 are popular job titles related to Quantum Machine Learning jobs in California?

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

What job categories do people searching Quantum Machine Learning jobs in California look for?

The top searched job categories for Quantum Machine Learning jobs in California are:

What cities in California are hiring for Quantum Machine Learning jobs?

Cities in California with the most Quantum Machine Learning job openings:

Quantum Machine Learning jobs near you
Infographic showing various Quantum Machine Learning job openings in California as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 21% Part Time, 2% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $42,026 per year, or $20.2 per hour.

Research Scientist - Frontier AI/ML & Quantum Algorithms

Sygaldry

San Francisco, CA • On-site

$200K - $300K/yr

Full-time

Medical, PTO

Re-posted 2 days ago

Job description

About Sygaldry
Sygaldry Technologies is building quantum-accelerated AI servers to exponentially speed up training and inference for AI. By integrating quantum and AI, we're accelerating the path to superintelligence, and addressing the problem of rising compute costs and energy bottlenecks. Sygaldry AI servers combine multiple qubit types within a single, fault-tolerant architecture to deliver the combination of cost, scale, and speed necessary for advanced AI applications. We pioneer new domains in physics, engineering, and AI, tackling the hardest challenges with a grounded, optimistic, and rigorous culture. We're looking for individuals ready to define the intersection of quantum and AI and drive its profound global impact.
About the Role
Frontier AI is moving toward scientific reasoning and design: molecules, materials, proteins, weather, climate, dynamical systems, quantum devices, and controlled physical systems. These domains expose deep computational bottlenecks in sampling, probabilistic inference, optimization, simulation, uncertainty quantification, inverse design, planning, and control.
Sygaldry is building quantum-accelerated AI systems for this next era. We are looking for a Research Scientist who can help define Quantum AI: not just quantum machine learning, but the broader study of how fault-tolerant quantum computation can transform the primitives of learning, inference, reasoning, prediction, geometry, and control.
In this role, you will work at the intersection of frontier AI/ML, quantum algorithms, scientific machine learning, and hardware-software co-design. You will identify where quantum computation can provide genuine structural advantage for AI workloads, develop new theoretical and empirical frameworks, and translate research insights into systems that inform real quantum hardware and AI architecture decisions.
What You'll Work On
Frontier AI for Scientific Discovery
Develop and study models for high-dimensional scientific prediction, generation, and design, including:
  • Diffusion models, flow matching, consistency models, score-based generative models, energy-based models, latent-variable models, autoregressive models, and normalizing flows.
  • Scientific foundation models for molecules, materials, proteins, quantum systems, weather, climate, PDEs, and dynamical systems.
  • Graph neural networks, geometric deep learning, equivariant models, neural operators, tensor methods, manifold learning, and learning on structured state spaces.
  • Models that combine prediction, uncertainty, active learning, and closed-loop design for scientific discovery.

Learning, Inference, and Reasoning
Build algorithms and theory for the computational primitives that matter most for next-generation AI systems:
  • Probabilistic inference, Bayesian modeling, variational inference, Monte Carlo methods, simulation-based inference, uncertainty quantification, and calibration.
  • Optimization, sampling, amortized inference, sequential decision-making, Bayesian experimental design, reinforcement learning, planning, and control.
  • Scientific reasoning systems, model-guided discovery, algorithmic discovery, and agents that can propose, test, and refine hypotheses.
  • Benchmarking frameworks that reveal when a new computational substrate changes scaling behavior, not just constant factors.

Quantum Algorithms for AI Workloads
Identify where quantum computation can accelerate or reshape AI-relevant subroutines, including:
  • Quantum algorithms for sampling, integration, Monte Carlo acceleration, linear algebra, optimization, Hamiltonian simulation, quantum simulation, and tensor-structured computation.
  • Fault-tolerant quantum algorithms, resource estimation, complexity analysis, block encoding, QSVT, LCU methods, amplitude estimation, phase estimation, and quantum walks.
  • Hybrid quantum-classical workflows where quantum primitives are embedded inside classical AI pipelines.
  • New quantum-native model classes, kernels, embeddings, generative processes, and inference procedures that are mathematically motivated rather than benchmark-driven alone.

Hardware-Software Co-Design
Collaborate closely with quantum architecture, systems, and hardware teams to connect AI workloads to real machine requirements:
  • Translate AI and scientific-computing bottlenecks into quantum resource requirements.
  • Design benchmarks that compare quantum, classical, and hybrid approaches under realistic assumptions.
  • Inform architecture choices by identifying the algorithms, error budgets, and primitives that matter for future AI workloads.
  • Build prototypes in Python/JAX/PyTorch and, when useful, quantum software frameworks such as PennyLane, Qiskit, Cirq, CUDA-Q, TensorCircuit, or custom simulators.
You May Be a Good Fit If You
  • Have a research record in machine learning, AI, statistics, physics, applied mathematics, computer science, quantum information, or a related field.
  • Have deep expertise in at least two of the following: generative modeling, probabilistic inference, uncertainty quantification, geometric deep learning, graph neural networks, optimization, reinforcement learning/control, numerical methods, scientific machine learning, quantum algorithms, or quantum information.
  • Have published research relevant to audiences at NeurIPS, ICML, ICLR, AISTATS, UAI, COLT, QIP, TQC, PRX Quantum, Nature, Science, or similar.
  • Can move between theory and implementation: deriving algorithms, building prototypes, running careful experiments, and communicating results clearly.
  • Are experienced with ML frameworks (PyTorch, JAX) and efficient inference implementation.
  • Are excited to work with quantum hardware teams and help define what AI workloads should demand from future fault-tolerant quantum systems.
  • Communicate complex ideas clearly across research communities
  • Value rigor: you are comfortable asking where quantum computation can help, where it cannot, and what evidence would distinguish the two.
Strong Candidates May Have
  • Research experience in diffusion/flow models, energy-based models, probabilistic programming, Bayesian deep learning, neural SDEs/ODEs, simulation-based inference, or scalable Monte Carlo.
  • Experience with AI for science: molecular design, protein design, drug discovery, materials discovery, weather or climate prediction, quantum chemistry, PDE modeling, dynamical systems, robotics, or control.
  • Experience with graph/geometric learning, equivariant architectures, neural operators, tensor networks, manifold methods, or structured world models.
  • Background in quantum algorithms, computational complexity, quantum simulation, quantum chemistry, fault tolerance, resource estimation, or quantum information theory.
  • Experience with JAX, PyTorch, CUDA/Triton, distributed training/inference, differentiable simulation, or high-performance scientific computing.
  • A track record of publishing, open-source software, or building research systems that influenced a field.

How We're Different
At Sygaldry, curiosity and intellectual courage drive our work. We approach ambitious challenges with a grounded, optimistic, and rigorous culture and know that kind people build the strongest teams. We prioritize mission over ego and collaborate openly with a strong sense of shared purpose. We dream big, yet we execute with a love of detail. We're looking for scientists, engineers, and operators to forge new paths with us at the intersection of quantum and AI.
Culture & Benefits
  • Visa Sponsorship - We know what it takes to make top talent thrive here. We're open to supporting visas whenever possible.
  • Compensation - We value your contribution and invest in your future with a competitive salary and meaningful equity.
  • Benefits - Your well-being matters. We provide company-sponsored health coverage to give you and your family peace of mind.
  • Connection - Whether it's company offsite or casual crew socials, we make time to connect, recharge, and have fun together.
  • Time Off - We trust you to take the time you need. Unlimited PTO so you can rest, recharge, and come back ready to make an impact.

We encourage applications from candidates with diverse backgrounds. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
We encourage you to apply even if you do not believe you meet every single qualification. If you don't think this role is right for you, but you believe that you would have something meaningful to contribute to our mission, please reach out at letsbuild@sygaldry.com

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