We developed an AI-based cough sound analysis technology to help identify at-risk Covid patients before administering lab-based tests, even if they’re asymptomatic.
Millions of people have died as a result of contracting Covid, while over 2 billion people have contracted the disease. When we started working on this technology in April 2020, the world was in the early stages of the pandemic. The need of the hour was to be able to test suspected cases rapidly so that Covid-positive cases could be isolated and further transmission minimised. Testing capacity was still limited, and ramping it up was expensive and time consuming, particularly in rural and remote areas. It was crucial to devise a simple, non-invasive triaging method that allowed the most probable suspected cases to be prioritised for Covid testing. Our solution was developed to support health systems in isolating and treating infected patients effectively.
Many parts of the world lack adequate supplies. Testing for Covid is an expensive undertaking that requires additional infrastructure and trained medical professionals, both of which may not be available. To add to this, the gold standard Real-time polymerase chain reaction (RT-PCR) test has a long lead time. This has left the world’s population at risk, especially those with comorbidities. The effective utilisation of resources continues to be critical, with each new wave and Covid variant.
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.