We are developing multiple AI solutions to reduce morbidity and mortality for mothers and children in low-resource settings by improving the quality of primary care, and strengthening the first 1,000 days of life.
In the domain of maternal, newborn, and child health, our mission is to use AI to reduce morbidity and mortality for mothers, newborns, and children under 6 years of age, in low-resource settings, by improving the quality of primary care, and strengthen the first 1000 days of life.
Our AI solution for estimating the weight of a baby learns to map a set of baby pictures or a video to the baby’s weight. Currently, we are exploring deep learning methods that take as input a set of video frames,and directly estimate the baby’s weight by performing implicit reasoning about the baby’s shape and volume. In the past, we have also explored 3D approaches that leverage a parametric, deformable 3D model to fit the baby’s appearance, followed by volume and weight estimation using a constant density. To this end, we are creating a 3D parametric model of Indian babies.
To obtain a better understanding of baby shape, the data presented to the model consists of video that captures the baby from a range of angles by sweeping a smartphone directly above a baby. Ground-truth for the AI system is obtained using accurate digital weighing scales, both in rural home and hospital settings. The approach also relies on having an object of standard dimensions placed next to the baby. This object serves as a reference to translate the scale of the baby from the image (pixels) space to a metric space, and is an important input to weight estimation.
We are developing a solution that uses computer vision to assist frontline health workers in measuring the height and weight of children from six months to six years of age in community settings leveraging video data obtained from smartphones.
We are working on an AI solution to enable the early detection of risk indicators in pregnant women and prediction of adverse outcomes for mothers and newborns. We have developed a technical proof-of-concept AI model that predicts a range of peripartum outcomes using synthetic intrapartum data with promising results.
ROLES AND RESPONSIBILITIES
An ML Engineer at Wadhwani AI will be responsible for building robust machine learning solutions to problems of societal importance; usually under the guidance of senior ML scientists, and in collaboration with dedicated software engineers. To our partners, a Wadhwani AI solution is generally a decision making tool that requires some piece of data to engage. It will be your responsibility to ensure that the information provided using that piece of data is sound. This not only requires robust learned models, but pipelines over which those models can be built, tweaked, tested, and monitored. The following subsections provide details from the perspective of solution design:
Early stage of proof of concept (PoC)
This is early to mid-stage of AI product development
Responsibilities during production deployment
We realize this list is broad and extensive. While the ideal candidate has some exposure to each of these topics, we also envision great candidates being experts at some subset. If either of those cases happens to be you, please apply.
Master’s degree or above in a STEM field. Several years of experience getting their hands dirty applying their craft.
ROLES AND RESPONSIBILITIES
As an ML Scientist at Wadhwani AI, you will be responsible for building robust machine learning solutions to problems of societal importance, usually under the guidance of senior ML scientists. You will participate in translating a problem in the social sector to a well-defined AI problem, in the development and execution of algorithms and solutions to the problem, in the successful and scaled deployment of the AI solution, and in defining appropriate metrics to evaluate the effectiveness of the deployed solution.
In order to apply machine learning for social good, you will need to understand user challenges and their context, curate and transform data, train and validate models, run simulations, and broadly derive insights from data. In doing so, you will work in cross-functional teams spanning ML modeling, engineering, product, and domain experts. You will also interface with social sector organizations as appropriate.
Associate ML scientists will have a strong academic background in a quantitative field (see below) at the Bachelor’s or Master’s level, with project experience in applied machine learning. They will possess demonstrable skills in coding, data mining and analysis, and building and implementing ML or statistical models. Where needed, they will have to learn and adapt to the requirements imposed by real-life, scaled deployments.
Candidates should have excellent communication skills and a willingness to adapt to the challenges of doing applied work for social good.