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Internship Ibm Quantum Machine Learning Jobs (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 ...

Ecole de technologie superieure

$100K/yr

... built by IBM via Pinq2. ETS is also home to the Centech, a business incubator, offering support ... Then consideration for complementary fields, including : * quantum machine learning; * quantum ...

Jane Street

Machine Learning Engineer

New York, NY · On-site

Many classes and activities are shared with our Software Engineering interns, while others focus specifically on machine learning applications and techniques. Machine learning is a critical pillar of ...

Jane Street

Machine Learning Engineer

New York, NY

Many classes and activities are shared with our Software Engineering interns, while others focus specifically on machine learning applications and techniques. Machine learning is a critical pillar of ...

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How much do internship ibm quantum machine learning jobs pay per year?

As of Jul 17, 2026, the average yearly pay for internship ibm quantum machine learning 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 an IBM Quantum Machine Learning Internship?

An IBM Quantum Machine Learning Internship is a temporary position for students or recent graduates to work alongside IBM researchers and engineers on projects at the intersection of quantum computing and machine learning. Interns typically contribute to developing algorithms, running experiments on real quantum hardware, and advancing the understanding of how quantum computers can enhance machine learning tasks. The internship provides hands-on experience with IBM's quantum technologies, including Qiskit, and offers opportunities to collaborate with leading experts in the field. Applicants generally need a background in computer science, physics, mathematics, or related fields, and some familiarity with quantum computing concepts.

What is the difference between Internship Ibm Quantum Machine Learning vs Data Science Intern?

AspectInternship Ibm Quantum Machine LearningData Science Intern
Required CredentialsBasic knowledge of quantum computing, programming, and machine learningBackground in statistics, programming, and data analysis
Work EnvironmentResearch-focused, technology-driven, often in labs or R&D teamsBusiness or research settings, analyzing large datasets
Industry UsageEmerging field within tech and research sectorsWidely used across industries like finance, healthcare, and tech
Search & Comparison IntentUnderstanding quantum ML internship opportunitiesExploring data science internship roles

Internship Ibm Quantum Machine Learning focuses on applying quantum computing techniques to machine learning problems, often requiring knowledge of quantum algorithms and programming. In contrast, Data Science Internships involve analyzing data, building models, and deriving insights using traditional data analysis tools. Both roles are research-oriented but differ in technical focus and industry application.

What kinds of projects or tasks can interns expect to work on during an IBM Quantum Machine Learning internship?

During an IBM Quantum Machine Learning internship, interns often collaborate with research scientists and engineers on projects that explore the intersection of quantum computing and machine learning. Typical responsibilities include implementing quantum algorithms, analyzing experimental data, developing proof-of-concept applications, and contributing to open-source software or research publications. Interns may also participate in team meetings, technical discussions, and code reviews, gaining exposure to cutting-edge quantum technologies and professional research environments. This hands-on experience provides valuable insight into both academic and industry applications of quantum machine learning.

What are the key skills and qualifications needed to thrive as an IBM Quantum Machine Learning Intern, and why are they important?

To excel as an IBM Quantum Machine Learning Intern, you typically need a background in computer science, physics, or a related field, with strong programming skills (Python) and foundational knowledge in quantum computing and machine learning. Familiarity with quantum programming frameworks such as Qiskit, as well as experience with machine learning libraries like TensorFlow or PyTorch, is highly beneficial. Strong analytical thinking, problem-solving abilities, and effective communication skills distinguish top candidates in this role. These competencies enable interns to contribute meaningfully to research projects, collaborate with interdisciplinary teams, and adapt to rapidly evolving technologies in quantum computing.
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Internship Ibm Quantum Machine Learning jobs near you
Infographic showing various Internship Ibm Quantum Machine Learning job openings in the United States as of July 2026, with employment types broken down into 44% Full Time, 54% Part Time, and 2% Contract. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Research Scientist - Frontier AI/ML & Quantum Algorithms

Sygaldry

San Francisco, CA • On-site

$200K - $300K/yr

Full-time

Medical, PTO

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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