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Machine Learning Quantum Computing Jobs in Windsor, CA

DD-Scientific Data

Scientific Data Engineer

Bodega Bay, CA

$135K - $163K/yr

Design, implement and maintain high performance computing and cloud solutions for visualization and analysis of complex biological data * Develop machine learning and AI solutions for analysis of ...

Agility Robotics

Senior Director, AI

Bodega Bay, CA · On-site +1

$257K - $402K/yr

Lead the selection and integration of advanced machine learning models, algorithms, and deep ... Forecast and manage the budget for cloud computing resources (GPUs, TPUs) necessary for large-scale ...

Kotoba

Artificial Intelligence Researcher

Santa Rosa, CA · On-site

A PhD or equivalent research experience in machine learning, speech processing, natural language ... Familiarity with large-scale model training, inference, data pipelines, distributed computing, and ...

Kotoba

Artificial Intelligence Researcher

Sonoma, CA · On-site

A PhD or equivalent research experience in machine learning, speech processing, natural language ... Familiarity with large-scale model training, inference, data pipelines, distributed computing, and ...

NE-NERSC

HPC/AI Performance Specialist

Bodega Bay, CA

... and Machine Learning on Doudna and future NERSC supercomputing systems. We're here for the same ... Mentor early-career staff in computing techniques and projects. * Track emerging HPC/AI trends and ...

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All JobsMachine Learning Quantum Computing JobsMachine Learning Quantum Computing Jobs in Windsor, CA

Machine Learning Quantum Computing information

See Windsor, CA salary details

$28K

$46.8K

$96.6K

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

As of Aug 26, 2026, the average yearly pay for machine learning quantum computing in Windsor, CA is $46,761.00, according to ZipRecruiter salary data. Most workers in this role earn between $35,700.00 and $50,500.00 per year, depending on experience, location, and employer.

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 the key skills and qualifications needed to thrive as a machine learning quantum computing specialist?

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 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 popular job titles related to Machine Learning Quantum Computing jobs in Windsor, CA?

For Machine Learning Quantum Computing jobs in Windsor, CA, the most frequently searched job titles are:

What cities near Windsor, CA are hiring for Machine Learning Quantum Computing jobs?

Cities near Windsor, CA with the most Machine Learning Quantum Computing job openings:

Machine Learning Quantum Computing jobs near you

Machine Learning / Data Scientist

PROPRIUS

Bodega Bay, CA

Full-time

Re-posted 10 days ago

Job description

Machine Learning Engineer

Location: San Francisco, CA

Sponsorship: No

Relocation: No

Industry: Machine Learning

Our client is a digital invention agency focused on machine learning methodologies, enterprise mobile and web applications, eCommerce, augmented reality and IoT. They look to innovatively make this world a better place with each and every product, system, idea and app they release.

Job Summary

Our client is looking for a machine learning engineer to join our existing ML team in developing and refining a predictive application.

The ideal candidate is adept at using large data sets to find opportunities for product and process optimization and using models to test the effectiveness of different courses of action.

You must have strong experience using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models, using/creating algorithms and creating/running simulations. You must have a proven ability to drive business results with their data-based insights. You must be comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.

As a ML Engineer, you will:

  • Work with stakeholders throughout the organization to identify opportunities for leveraging data to drive business solutions
  • Mine and analyze data from databases to drive optimization and improvement of product development, marketing techniques and business strategies
  • Assess the effectiveness and accuracy of new data sources and data gathering techniques
  • Develop custom data models and algorithms to apply to data sets
  • Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes
  • Coordinate with different functional teams to implement models and monitor outcomes
  • Develop processes and tools to monitor and analyze model performance and data accuracy

For this role you will need:

  • Strong with Statistics and can code in either R, Python, Java and Scala
  • Experience with designing and building using micro-services architectural pattern, web APIs using dotnet core & C#
  • Experience and passion for simulations, optimization, neural networks, artificial intelligence (deep learning and machine learning)
  • Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, GIT, SQL, etc.
  • Able to understand statistical solutions and execute similar activities
  • Experience in data wrangling and advanced analytic modeling
  • Strong communication and organizational skills and has the ability to deal with ambiguity while juggling multiple priorities and projects at the same time
  • Experience visualizing/presenting data for stakeholders using: Seaborn, Business Objects, D3, ggplot, etc.
  • Ability to investigate the feasibility and data requirements necessary to develop an ML solution for a given problem
  • Ability to design, build and test production ready ML-based products while interpreting and explaining the basis for predictions generated by ML models

The perfect candidate will have:

  • Knowledge and experience using one or more of the following, or similar, machine learning software frameworks: CAFFE, Torch 7, Keras and Tensorflow
  • Experience building production-ready NLP or information retrieval systems
  • Hands-on experience with NLP tools, libraries and corpora (e.g. NLTK, Stanford CoreNLP, Wikipedia corpus, etc.)

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