HOPPR

πŸ‘₯ 11-50Artificial Intelligence (AI)MedicalInformation TechnologyHealth CareHealth DiagnosticsSoftwareπŸ’Ό Private Company
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HOPPR is a company that provides a secure platform with data, foundation models, and tools aimed at enhancing app development through improved accuracy and performance.

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πŸ“ United States

πŸ” Healthcare Technology

  • Recognized industry expert with 15+ years of experience in ML research and engineering, with 8+ years in a leadership role
  • PhD in Computer Science, Machine Learning, Biomedical Engineering, or a related field
  • Proven ability to drive high-impact research as both a lead author and as a mentor or advisor demonstrated by top-tier publications (NeurIPS, ICML, CVPR, Nature Medicine)
  • Demonstrable experience developing and deploying SOTA multi-modal foundation models in healthcare. Extensive hands-on experience with modern deep learning frameworks (PyTorch, TensorFlow) and medical imaging libraries (MONAI, DICOM, ITK)
  • Deep expertise in transformer architectures, self-supervised learning, multi-modal fusion, and foundation models
  • Proven experience scaling ML research to production systems
  • Experience with MLOps, cloud-based ML pipelines, and model deployment in production environments (AWS/GCP/Azure)
  • Lead and mentor a team of ML engineers, fostering a culture of innovation and technical excellence.
  • Architect and optimize multi-modal deep learning models.
  • Oversee the end-to-end ML pipeline, including data preprocessing, model training, evaluation, and deployment.
  • Drive the integration of AI models into the HOPPR platform, ensuring seamless interoperability.
  • Collaborate with clinicians and regulatory teams to ensure AI models meet medical and compliance standards (e.g., FDA, HIPAA).
  • Optimize models for real-world performance, focusing on generalizability, robustness, and explainability.
  • Lead initiatives in model interpretability, bias mitigation, and continual learning.
  • Scale ML infrastructure and ML Ops best practices for efficient model development and deployment.
  • Stay at the forefront of ML advancements and implement cutting-edge techniques in deep learning and medical imaging AI.
  • Work closely with cross-functional teams, including product, engineering, and regulatory teams, to align AI solutions with business goals.

AWSCloud ComputingGCPMachine LearningPyTorchAzureTensorflowData modeling

Posted 2 months ago
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πŸ”₯ ML Engineer
Posted 3 months ago

πŸ“ United States

🧭 Full-Time

πŸ” Medical Imaging, AI/ML

  • Master’s or PhD in Computer Science, Engineering, or a related field
  • 3+ years' experience in relevant roles
  • Proficiency in Python and machine learning frameworks such as PyTorch or TensorFlow
  • Strong understanding of MLOps practices
  • Experience with cloud platforms (e.g., AWS, GCP, Azure)
  • Knowledge of data engineering principles
  • Develop, deploy, and maintain state-of-the-art machine learning models
  • Design and implement robust ML pipelines and shared infrastructure
  • Collaborate with researchers to translate algorithms into production-ready solutions
  • Build and maintain MLOps tools and practices
  • Optimize model performance in production environments
  • Partner with clinicians and product teams

AWSDockerPythonSQLGCPKubernetesMachine LearningPyTorchAzurePandasTensorflow

Posted 3 months ago
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πŸ”₯ Data Scientist
Posted 3 months ago

🧭 Full-Time

πŸ” Medical Imaging

  • Master’s or PhD in Computer Science, Engineering, Data Science, or a related field.
  • 1+ years of professional data science experience, with a proven ability to train, evaluate, and deploy machine learning models, including large language models.
  • Proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow), as well as experience with data manipulation tools like SQL, pandas, or NumPy.
  • Familiarity with ML Ops practices and deploying models into production pipelines (preferred).
  • Knowledge of healthcare data, such as radiology images or EHRs, is a plus.
  • Design and develop robust pipelines using advanced methods with large language models (LLMs) to extract features and label data from unstructured datasets
  • Create and implement rigorous evaluation metrics to assess feature extraction processes, ensuring continuous improvement aligned with clinical and product goals.
  • Enhance and maintain scalable, reproducible data science infrastructure to support agile development and secure operations across partitioned client environments.
  • Design and implement MLOps practices to streamline, scale, and automate machine learning workflows.
  • Manipulate, analyze, and manage large-scale datasets using Python, SQL, and other tools.
  • Work closely with engineers, clinicians, and product teams to ensure data solutions are aligned with user needs and drive meaningful outcomes.
Posted 3 months ago
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