We are a Google AI Impact grantee

Wadhwani AI will receive a $2M USD grant to create technologies that will help reduce crop losses in cotton farming, through integrated pest management.
Wadhwani AI will receive a $2M USD grant to create technologies that will help reduce crop losses in cotton farming, through integrated pest management.

We are thrilled to share that Wadhwani Institute for Artificial Intelligence is one of 20 organizations that will share $25 million in grants from Google.org, credit and consulting from Google Cloud and coaching by Google’s AI experts as a grantee of the Google AI Impact Challenge.

Wadhwani AI will receive a $2M USD grant to create technologies that will help reduce crop losses in cotton farming, through integrated pest management.

More than a billion people live in smallholder farmer households worldwide, and many of these farmers struggle with avoidable pest damage that can wipe out up to 50% of annual crop yield. For example, in India, for the 30 million people – 6 million farmers and their families – that depend on cotton farming for a living, inability to manage pests effectively is one the biggest risks. This, despite the fact that cotton accounts for close to half of India’s pesticide usage.

In our project, AI technology which runs on a basic smartphone, classifies and counts pests based on photos of pest traps taken by farmers and agriculture program workers. This solution can be used to provide millions of farmers with timely, localized advice, reducing crop loss and over-use of pesticides by improving the timing of usage.

Our partners are the Government of Maharashtra and members of the Better Cotton Initiative, who have facilitated farmer interactions and data collection, and will ultimately be able to integrate the solution into their programs. The project has the potential to develop a template that can be replicated in large-scale agriculture programs worldwide.

“We received thousands of applications to the Google AI Impact Challenge and are excited that Wadhwani AI was selected to receive funding and expertise from Google. AI is at a nascent stage when it comes to the value it can have for the social impact sector, and we look forward to seeing the outcomes of this work and considering where there is potential for use to do even more.”

— Jacquelline Fuller, President of Google.org

Raghu Dharmaraju, VP Products and Programs, said, “Small farmers worldwide depend heavily on government and nonprofit programs to figure out what to do at every step of the crop cycle. By using AI to augment human capabilities and overcome systemic challenges in these large-scale programs, we can help millions of farmers. Pest management is just the beginning.

Dr. P Anandan, CEO, said, “Wadhwani AI’s mission is to use AI to help improve the lives of the billions of poor and underserved communities throughout the world. Agriculture is one of the critical domains in which we apply our efforts. We are grateful to Google and delighted to have their support and the benefit of their deep expertise and experience in developing AI solutions at scale.

Next week, some of our colleagues will travel to San Francisco to dive into execution. For five days, all 20 organizations will join Google AI experts, Project Managers and the startup specialists from Google’s Launchpad Accelerator for a program that will last six months, from May to November 2019.

We look forward to sharing the journey with all of you!

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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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