As a collective endeavor to seek out higher truth, maybe some amount of fraud is necessary to train the immune system of the collective body, so to speak, so that it's more resilient in the long-term. But too much fraud, I agree, could tip into mistrust of the entire system. My fear is that AI further exacerbates this problem, and only AI itself can handle wading through the resulting volume of junk science output.
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MD.ai helps doctors, scientists, and engineers build medical AI that have potential to improve patient care and outcomes. Our overarching goal is to accelerate medical AI development, deployment, and validation. We provide software tools to enable large-scale collaborative dataset curation and annotation as well as model deployment and federated clinical validation, with a particular focus on medical imaging. Our software platform is used by top academic medical institutions as well as large pharmaceutical and healthcare companies.
We're looking for talented full-stack and infrastructure/devops engineers interested in ML/AI and healthcare to join our growing team. You'll have an opportunity to take on significant ownership over product and code, help drive our engineering culture, and really make an impact. Our tech stack includes: React, TypeScript, WebGL, PostgreSQL, Redis, Python, PyTorch, Kubernetes, Terraform, AWS/GCP/Azure.
We are a medical AI development platform (https://www.md.ai), currently focused on medical imaging (radiology). We help build high-quality labeled datasets for both training and clinical validation, as well as provide tools and infrastructure for deploying and running models at scale. Some of our unique challenges include: operating in HIPAA-compliant environments, working with large medical imaging/text/genomic datasets, managing machine learning model lifecycles, and building complex web applications with UI/UX appealing to both doctors and engineers alike.
We currently have availability for summer interns with experience with JavaScript/TypeScript/React/GraphQL/DICOM.
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We are a medical AI development platform (https://www.md.ai), currently focused on radiology/pathology/dermatology. We help build high-quality labeled datasets for both training and clinical validation, as well as provide tools and infrastructure for deploying and running models at scale. Some of our unique challenges include: operating in HIPAA-compliant environments, working with large medical imaging/text/genomic datasets, managing machine learning model lifecycles, and building complex web applications with UI/UX appealing to both doctors and engineers alike.
We currently have availability for summer interns with skills in React, GraphQL, Kubernetes, Docker, Terraform, GCP/AWS/Azure, TensorFlow/PyTorch, or medical imaging.
MD.ai | Software Engineer | Full-time | New York, NY / Seattle, WA | Onsite OR Remote (USA only)
We are a medical AI development platform (https://www.md.ai), currently focused on radiology/pathology/dermatology. We help build high-quality labeled datasets for both training and clinical validation, as well as provide tools and infrastructure for deploying and running models at scale. Some of our unique challenges include: operating in HIPAA-compliant environments, working with large medical imaging/text/genomic datasets, managing machine learning model lifecycles, and building complex web applications with UI/UX appealing to both doctors and engineers alike.
We are currently looking for front-end developers (React, GraphQL) and software engineers experienced in devops/cloud technologies (Kubernetes, Docker, Terraform, GCP/AWS/Azure).
DeBakey performed his last surgery at age 90 [1], pioneered numerous procedures -- including one performed on himself at age 97 [2], and continued to practice medicine until his death at age 99. Remarkable person, but obviously an outlier. Just thought it would be interesting to bring him up in this thread.
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We are a medical machine learning platform helping doctors and researchers build medical AI. Our focus is on creating high-quality labeled datasets for training and clinical validation, and building tools for model development, training, deployment and validation. Some of our unique challenges include: operating in HIPAA-compliant environments, managing huge medical imaging/text/genomic datasets, distributed data processing and machine learning model training, and building complex web applications with UI/UX appealing to both doctors and engineers alike.
We're currently looking for highly motivated front-end or full-stack engineers (React/Vue/GraphQL) to join our growing team.
