Cough Against COVID

We are announcing the launch of a global data-crowdsourcing and open-innovation initiative to try and build an Artificial Intelligence (AI) tool that uses cough sounds, symptoms, and other contextual information to screen for possible COVID-19 infection.
Our global data crowdsourcing campaign #Coughagainstcovid aims to collect samples of cough sounds from people who have been tested for COVID-19, as well as those who haven’t.

As you read this, there are over 1.3 million active COVID-19 cases in the world. More than 70,000 people have succumbed to COVID-19.

It is becoming clear that much of the world does not, and will not, have the capacity to test enough people who show early symptoms of the COVID-19 infection. There are too many people with fever and cough at any given point of time in a country. Yet, detecting it early is an important means of preventing the transmission of COVID-19.

Today, we are announcing the launch of a global data-crowdsourcing and open-innovation initiative to try and build an Artificial Intelligence (AI) tool that uses cough sounds, symptoms, and other contextual information to screen for possible COVID-19 infection. Such a tool requires nothing more than a phone and can be used by people to self-screen. It will also help healthcare systems save limited tests for the most likely cases.

Our global data crowdsourcing campaign #Coughagainstcovid aims to collect samples of cough sounds from people who have been tested for COVID-19, as well as those who haven’t. Cough sounds carry vital information about the respiratory tract, and anecdotal evidence suggests that a COVID-19 patient’s cough sounds different from other coughs. The goal of #coughagainstcovid is to collect and analyze cough sounds to try and find the early signs of COVID-19 through AI.

Who are we? We are a not-for-profit, AI-for-social-good research institute. This initiative is supported by the Bill and Melinda Gates Foundation, and our collaborators include Stanford University. Collectively, we have world class capabilities in AI, medical technology, public health, and large-scale global deployment.

All data collected will be made freely available to researchers across the world (after anonymization) for open innovation. A global crisis calls for global collaboration.

We do not yet know if this is technically feasible. But we know it is not impossible. The only real way to know is to try.

A time like this calls for moonshots.

I have dedicated virtually all my professional life to large-scale breakthrough technologies for social good. This pandemic that has brought untold suffering to our world and it requires each of us to bring out the very best in ourselves.

Join us in humanity’s fight against this virus.

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ML Engineer

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)

  • Setup and structure code bases that support an interactive ML experimentation process, as well as quick initial deployments
  • Develop and maintain toolsets and processes for ensuring the reproducibility of results
  • Code reviews with other technical team members at various stages of the PoC
  • Develop, extend, adopt a reliable, colab-like environment for ML

Late PoC

This is early to mid-stage of AI product development

  • Develop ETL pipelines. These can also be shared and/or owned by data engineers
  • Setup and maintain feature stores, databases, and data catalogs. Ensuring data veracity and lineage of on-demand pulls
  • Develop and support model health metrics

Post PoC

Responsibilities during production deployment

  • Develop and support A/B testing. Setup continuous integration and development (CI/CD) processes and pipelines for models
  • Develop and support continuous model monitoring
  • Define and publish service-level agreements (SLAs) for model serving. Such agreements include model latency, throughput, and reliability
  • L1/L2/L3 support for model debugging
  • Develop and support model serving environments
  • Model compression and distillation

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.

DESIRED QUALIFICATIONS

Master’s degree or above in a STEM field. Several years of experience getting their hands dirty applying their craft.

Programming

  • Expert level Python programmer
  • Hands-on experience with Python libraries
    • Popular neural network libraries
    • Popular data science libraries (Pandas, numpy)
  • Knowledge of systems-level programming. Under the hood knowledge of C or C++
  • Experience and knowledge of various tools that fit into the model building pipeline. There are several – you should be able to speak to the pluses and minuses of a variety of tools given some challenge within the ML development pipeline
  • Database concepts; SQL
  • Experience with cloud platforms is a plus
mle

ML Scientist

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.  

REQUIREMENTS

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. 

DESIRED QUALIFICATIONS

  • B.Tech./B.E./B.S./M.Tech./M.E./M.S./M.Sc. or equivalent in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, Physics, Economics, or a relevant quantitative field. Work experience beyond the terminal degree will determine the appropriate seniority level.
  • Solid software engineering skills across one or multiple languages including Python, C++, Java.
  • Interest in applying software engineering practices to ML projects.
  • Track record of project work in applied machine learning. Experience in applying AI models to concrete real-world problems is a plus.
  • Strong verbal and written communication skills in English.
mls