7 Questions That Shape Our Work in AI

At Wadhwani AI, we use 7 key questions to identify domains in which we can help the underserved billions with AI solutions to catalyse large-scale impact.
At Wadhwani AI, our expertise is spread across AI research, data science, engineering, design, products, and programs.

When you have teams that focus on channeling the vast power of Artificial Intelligence for social good, across domains such as maternal and child health, cotton farming, and tuberculosis, it is vital to have a framework that everyone can work within, no matter what their individual projects are. At Wadhwani AI, our expertise is spread across AI research, data science, engineering, design, products, and programs.

We’ve created a set of anchoring principles, if you will, that serve as our touchstone before we decide to embark on a project:

1. IS THIS A BIG PROBLEM?

At Wadhwani AI, we measure our success based on the impact we are able to catalyse, and keep the communities we are trying to help, at the core of our solutions. We aim to take on truly large problems that affect the developing world, and use our global lens to then create solutions that can be scaled and are applicable across the world.

2. DOES IT HAVE AN AI SOLUTION?

This is a crucial question. For example, when we decided to work on a program centred around tuberculosis, we had to examine all aspects of the issue. To start off, we examined how TB is diagnosed—one way is by analysing the patient’s sputum, but this is already automated and widely so. Image-recognition algorithms also exist that can count the number of bacteria. But where could Wadhwani AI play a role? A few weeks of research later, we had the answer: nearly as many as 50% of TB cases may go unreported and unknown, and we could help change this number.

3. WILL SOLVING THE AI PART MAKE ENOUGH OF A DIFFERENCE?

Earlier this year, Wadhwani AI became the official AI partner for India’s Central Tuberculosis Division. Now, we are creating technologies to address multiple challenges starting with case load estimation and prioritisation of TB patients for health workers.

4. WILL THE SOLUTION BE ACCEPTED BY STAKEHOLDERS?

In the case of our tuberculosis project, we know we can make a difference, and that we are an acceptable part of the system to the other stakeholders. This is not always the case, as we learnt when we considered the possibility of addressing clinical support decision systems for doctors in primary healthcare centres through AI. While we could have automated a lot of the routine diagnosis and treatment processes, doctors were not open to the idea at all. Their reason was that too much automation would make patients lose faith in the doctors and their expertise.

5. DOES THE DATA EXIST, OR CAN IT BE CREATED EASILY ENOUGH?

Understanding issues such as these, and getting our research and data right is only possible when we find the right partners. We find that the best way for us is to identify people and organisations who have been working in the field for decades. Their deep understanding of the communities and the problems are invaluable to us because data about issues that are of societal consequence does not always exist. AI systems function on data, so at Wadhwani AI, research and data collection are key.

6. ARE THERE PARTNER ORGANISATIONS WHO CAN CO-CREATE AND PILOT?

Our partners in our Maternal and Child Health project are a good example of this. We could plug into the system, and create a mechanism that works within the existing apps. Plus, this is also a domain that is of global importance and consequence, so our work can be implemented internationally.

7. ARE THERE EXISTING PROGRAMS AND PATHWAYS TO SCALE?

Our ideal project is one in which we don’t have to create an app, and we don’t get stuck in the land of pilots. We aim to scale up as quickly as possible and take the project from an Indian context to a global one.

As a team we use these seven key questions to identify domains in which we can help the underserved billions by creating solutions that impact their lives in a meaningful way.

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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.
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