Introduction
Synapsica is a series-A funded HealthTech startup founded by alumni from IIT Kharagpur, AIIMS New Delhi, and IIM Ahmedabad. We believe healthcare needs to be transparent and objective while being affordable. Every patient has the right to know exactly what is happening in their bodies and they don't have to rely on cryptic 2 liners given to them as a diagnosis.
Towards this aim, we are building an artificial intelligence enabled cloud based platform to analyse medical images and create v2.0 of advanced radiology reporting. We are backed by IvyCap, Endia Partners, YCombinator and other investors from India, US, and Japan. We are proud to have GE and The Spinal Kinetics as our partners. Here’s a small sample of what we’re building: https://www.youtube.com/watch?v=FR6a94Tqqls
Your Roles and Responsibilities
Synapsica is looking for a Principal AI Researcher to lead and drive AI based research and development efforts. Ideal candidate should have extensive experience in Computer Vision and AI Research, either through studies or industrial R&D projects and should be excited to work on advanced exploratory research and development projects in computer vision and machine learning to create the next generation of advanced radiology solutions.
The role involves computer vision tasks including development customization and training of Convolutional Neural Networks (CNNs); application of ML techniques (SVM, regression, clustering etc.), and traditional Image Processing (OpenCV, etc.). The role is research-focused and would involve going through and implementing existing research papers, deep dive of problem analysis, frequent review of results, generating new ideas, building new models from scratch, publishing papers, automating and optimizing key processes. The role will span from real-world data handling to the most advanced methods such as transfer learning, generative models, reinforcement learning, etc., with a focus on understanding quickly and experimenting even faster. Suitable candidate will collaborate closely both with the medical research team, software developers and AI research scientists. The candidate must be creative, ask questions, and be comfortable challenging the status quo. The position is based in our Bangalore office.
Primary Responsibilities
- Interface between product managers and engineers to design, build, and deliver AI models and capabilities for our spine products.
- Formulate and design AI capabilities of our stack with special focus on computer vision.
- Strategize end-to-end model training flow including data annotation, model experiments, model optimizations, model deployment and relevant automations
- Lead teams, engineers, and scientists to envision and build new research capabilities and ensure delivery of our product roadmap.
- Organize regular reviews and discussions.
- Keep the team up-to-date with latest industrial and research updates.
- Publish research and clinical validation papers
Requirements
- 6+ years of relevant experience in solving complex real-world problems at scale using computer vision-based deep learning.
- Prior experience in leading and managing a team.
- Strong problem-solving ability
- Prior experience with Python, cuDNN, Tensorflow, PyTorch, Keras, Caffe (or similar Deep Learning frameworks).
- Extensive understanding of computer vision/image processing applications like object classification, segmentation, object detection etc
- Ability to write custom Convolutional Neural Network Architecture in Pytorch (or similar)
- Background in publishing research papers and/or patents
- Computer Vision and AI Research background in medical domain will be a plus
- Experience of GPU/DSP/other Multi-core architecture programming
- Effective communication with other project members and project stakeholders
- Detail-oriented, eager to learn, acquire new skills
- Prior Project Management and Team Leadership experience
- Ability to plan work and meet the deadline
About Synapsica Healthcare
At Synapsica, we are creating AI-first PACS and radiology workflow solution that is fast, secure and automates reporting tasks, helping radiologists create high quality reports quicker.
Our goal is to enable radiologists with fast, easy to use AI technology that helps generate high quality, evidence-based reports to significantly improve patient care.
Synapsica is a growth stage HealthTech startup founded by alumni from AIIMS, IIT-KGP & IIM-A with a vision to increase the accessibility of diagnostic services globally. We are solving the problem of shortage of radiologists by bringing in automation at various stages of the radiology reporting process to reduce reporting time, costs & error rates & deliver efficiencies for better patient care.
We are deploying Computer Vision based Neural Networks models to identify biomarkers of pathologies in Radiology scans. These models not just identify pathologies, but provide detailed characterization of what exactly constitutes that pathology.
RADIOLens is an intuitive, AI enabled, cloud based RIS/PACS solution that makes diagnostic radiology workflows smoother. It provides Faster image uploading with Zero loss in image quality. RADIOLens helps automate mundane reporting tasks so radiologists can focus on clinical correlations for their patients.
Spindle for MRI Spine helps with reporting of age related degeneration of spine. It automatically identifies key spinal mensurations and provides a detailed, standardized report with annotated images of abnormal various spinal elements. It quickly identifies variations and pathologies, such as spinal deformations, degeneration, identification of listhesis, etc.
SpindleX for stress X-Rays of spine takes automation a step further by generating a one click automated reporting for all XR cases. These reports quantify all abnormalities and features of injury or early degeneration such as spinal instability, abnormal intersegmental motion etc. Illustrative graphs and tables comparing extent of injury against standards are automatically included in the report making it easier to generate qualitative reports with visual evidence.
Crescent segregates normal, abnormal and bad quality captures in chest X-rays. It identifies bad quality scans that need re-capture and for good scans it localizes and characterizes common lesions & abnormalities. With Crescent, standard, pre-filled reports are generated for normal radiographs and critical studies are prioritized in the worklist.
We are backed by Y Combinator and other investors from India, US and Japan. We are proud to have GE, AIIMS, the Spinal Kinetics as our partners.
Here’s a small sample of what we’re building: https://youtu.be/MtWSF-x2sxY
Join us, if you find this as exciting as we do!
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