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Machine Learning Quantum Computing Jobs in Oregon

Natera

OR

Natera is hiring a Machine Learning Scientist to join our AI and computational biology team. This ... Experience managing datasets and training workflows within distributed or cloud computing ...

Natera

OR · On-site

We are seeking a Staff Machine Learning Scientist - Translational AI to provide technical ... Utilize cloud-based productivity and high-performance computing infrastructure to maintain high ...

Striim, Inc.

OR

$120K - $130K/yr

... computing, and real-time data streaming. Requirements * 1-3 years of professional experience or equivalent graduate research, internships, or project experience in data science, machine learning ...

Netflix

OR · On-site

$466K - $750K/yr

... machine learning libraries TensorFlow, PyTorch, JAX or Keras Experience with cloud computing platforms like AWS Background in math, statistics, or numerical computation Significant contributions to ...

Varsity Tutors

Linear Algebra Tutor

Portland, OR · Remote

$18 - $40/hr

... machine learning, and quantum mechanics applications. * Curriculum Awareness & Adaptive Instruction ... vector spaces, computing determinants of large matrices, and grasping the significance of ...

Varsity Tutors

Linear Algebra Tutor

OR · Remote

$18 - $40/hr

... machine learning, and quantum mechanics applications. * Curriculum Awareness & Adaptive Instruction ... vector spaces, computing determinants of large matrices, and grasping the significance of ...

Varsity Tutors

Linear Algebra Tutor

Eugene, OR · Remote

$18 - $40/hr

... machine learning, and quantum mechanics applications. * Curriculum Awareness & Adaptive Instruction ... vector spaces, computing determinants of large matrices, and grasping the significance of ...

Nike

Senior Software Engineer

Beaverton, OR · On-site

$127K - $168K/yr

... computing, machine learning, and emerging technologies; communicate effectively with cross-functional teams, building trust and strong relationships across the organization. Telecommuting is ...

BetterHelp

OR

Requirements * 3+ years of experience in machine learning platform systems * Experience with autoscaling, and autoscaling such as load balancing * Solid understanding of distributed computing and ...

Netflix

OR

$466K - $750K/yr

Applied Machine Learning Research at Netflix drives various aspects of our business, including ... computing platforms and large web-scale distributed systems Applied research experience in ...

Smarsh

OR

Development of machine learning models and other analytics following established workflows, while ... Familiarity with cloud computing platforms (AWS, GCS, Azure) * Experience with automated ...

Nvidia

OR

More recently, GPU deep learning ignited modern AI - the next era of computing. NVIDIA is a "learning machine" that constantly evolves by adapting to new opportunities that are hard to solve, that ...

Aptive

The ideal candidate can evaluate when statistical, AI, or machine learning approaches are ... Advanced proficiency in R for statistical computing, modeling, reproducible analytics, and package ...

Deloitte

Senior AI Engineer - SFL Scientific

Portland, OR

$110K - $152K/yr

Work with clients to design, develop, and deploy new architectures to support machine learning ... using cloud computing or on-prem technologies * Design and lead development on scalable, high ...

Cornerstone

$114K - $137K/yr

Experience building and deploying machine learning models in production. Strong proficiency in ... Experience with working on large data sets and distributed computing (e.g. Hive/Hadoop/Spark/Presto ...

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Machine Learning Quantum Computing information

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.
What are popular job titles related to Machine Learning Quantum Computing jobs in Oregon? For Machine Learning Quantum Computing jobs in Oregon, the most frequently searched job titles are:
What job categories do people searching Machine Learning Quantum Computing jobs in Oregon look for? The top searched job categories for Machine Learning Quantum Computing jobs in Oregon are:
What cities in Oregon are hiring for Machine Learning Quantum Computing jobs? Cities in Oregon with the most Machine Learning Quantum Computing job openings:
Machine Learning Quantum Computing jobs near you

Machine Learning Scientist, Multimodal AI

Natera

OR

Other

Re-posted 3 days ago

Natera rating

7.6

Company rating: 7.6 out of 10

Based on 37 frontline employees who took The Breakroom Quiz

60th of 120 rated laboratories

Job description

POSITION SUMMARY:

Natera is hiring a Machine Learning Scientist to join our AI and computational biology team. This role develops and deploys deep learning models across digital pathology, genomics, transcriptomics, and cell-free DNA (cfDNA) modalities. You will build multimodal AI systems that integrate imaging, molecular, and clinical data, leveraging proprietary genomic and clinical datasets. You will collaborate with scientists, pathologists, bioinformaticians, and software engineers to scale machine learning approaches that advance personalized oncology diagnostics and tumor-informed minimal residual disease (MRD) testing.

PRIMARY RESPONSIBILITIES:

  • Design, implement, and evaluate deep learning models across biomedical data modalities, including histopathology imaging, genomic sequencing, transcriptomics, and cfDNA features
  • Develop multimodal AI architectures that integrate H&E whole-slide imaging data with molecular and clinical data sources
  • Build scalable, production-quality machine learning workflows and pipelines using cloud infrastructure (AWS)
  • Apply modern machine learning techniques including convolutional neural networks (CNNs), vision transformers (ViTs), sequence transformers, representation learning, and foundation model fine-tuning
  • Collaborate across technical and clinical teams to translate machine learning prototypes into validated tools
  • Analyze model outputs to generate reproducible biological and clinical insights
  • Document pipelines thoroughly and communicate data-driven findings clearly to cross-functional stakeholders

QUALIFICATIONS:

  • PhD in Computer Science, Computational Biology, Biomedical Engineering, Bioinformatics, Statistics, or a related quantitative discipline with a focus on machine learning or AI
  • Core experience developing machine learning models for biomedical applications, specifically in medical imaging, computational pathology, genomics, transcriptomics, multi-omics, or molecular diagnostics
  • Hands-on expertise with PyTorch and strong production-level programming skills in Python
  • Practical application of deep learning architectures such as CNNs, transformers, attention mechanisms, and representation learning
  • Experience managing datasets and training workflows within distributed or cloud computing environments (AWS)
  • Proven ability to take ownership of research projects and translate prototypes into robust, deployment-ready workflows
  • Experience adapting pre-trained foundation models for downstream biomedical applications

PREFERRED QUALIFICATIONS:

  • Experience integrating imaging, molecular, and clinical data within unified multimodal machine learning frameworks
  • Technical familiarity with DNA sequencing, RNA sequencing, methylation, and ctDNA assays
  • Hands-on experience with digital pathology software and whole-slide imaging analysis
  • Exposure to survival modeling, longitudinal prediction, or time-to-event modeling
  • Experience applying self-supervised learning, weakly supervised learning, or multiple instance learning (MIL) to clinical data
  • Domain knowledge in oncology, biomarker discovery, or clinical precision medicine
  • Track record of peer-reviewed publications in machine learning or computational biology conferences and journals (e.g., NeurIPS, ICML, CVPR, MICCAI, Nature Biomedical Engineering)

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