Uber: Made in India, Made for the World

Uber Cash and Uber Lite are just some of the innovations that came out of India and were used all over the world, showing that the Indian R&D scene has come of age. Apurva Dalal, Site lead for Bangalore, Uber, talks of their company’s ambitious vision and their India R&D centers’ roles in realizing that vision.

Uber Cash and Uber Lite are some of the Made in India, Made for the World solutions. How did they come about and what were the challenges they faced?

At Uber Engineering, our designing and engineering teams collaborated and worked in sync from the beginning to create what we believe is a strong product. What we realized through our core app was that there was a need and opportunity to address commute constraints in certain markets. We noticed that there was a certain demographic of users in emerging markets, who faced three core issues—affordability, connectivity, and simplicity. We noticed that users weren’t successfully booking rides, particularly in countries dominated by Android phone users. More than a third of the users from emerging markets come from connectivity zones that are lesser than 3G. To understand these users, team from India traveled to a handful of cities in India and in Latin America to meet users face to face and understand what their issues were with using Uber. Their insights led to a complete revamp of the core Uber app, called Uber Lite, which launched as a pilot in India in summer 2018. Now, a year into the launch, Uber Lite is available in 30 countries, in English, Spanish, Portuguese, and Arabic (Hindi is coming soon). Since the end of 2018, the number of Uber Lite downloads has increased 300%.

70% of the users in these markets are on Android phones or low end devices that faced computing and processing challenges in addition to the ones mentioned above. More than a third of these users come from connectivity zones that are lesser than 3G. We noticed that this demographic of users were confused with how maps function, and so after our user research, we reinterpreted the design of the app to cater to their comfort. We made the map less congested, with lesser pointers and heavily relied on points of interest (POIs) as we realized that most riders feel more comfortable with that kind of location tracking.

What kind of talent do you look for when you hire for your R&D and development centers?

We are in a growth phase and hiring for right talent continues—we have more than doubled every year and continue on the rapid expansion path. Given our bar is very high, only the top 5-10% of tech talent makes the cut and that is our main challenge in going faster.

We are constantly hiring at all levels—from best campuses and lateral hiring, backend/frontend/mobile app development (Android/iOS) being important divisions. In campus hires we are mainly looking for solid coding skills—ability to write clean code fast. In laterals we are agnostic on prior programming language (Python, Java, C++, Golang..) but have a high bar on design/architecture, building for efficiency/reuse, collaboration, communication and leadership skills.

Besides engineering, we are also hiring for product management, data science/ML, research and product design experts along with individual contributors as leadership hires—people with solid leadership and management along with prior experience in scaling systems, teams and processes in a true-blue technology organization.

What are the challenges of Uber copter and can we have an Uber plane in the future?

Uber Air is our most ambitious vision for that future because it takes Uber’s tech into the skies by making it possible for people to push a button and get an affordable flight. We are working with government and industry stakeholders to create the world’s first aerial rideshare network with safe, quiet, and electric Uber Air vehicles transporting tens of thousands of people across cities at affordable prices.

Over the next few years, Uber will continue to work closely with city and country stakeholders to ensure that we create an urban aviation rideshare network that is safe, quiet, environmentally conscious and supports multi-modal transportation options.

What are the biggest technological challenges faced by mobile real-time apps like Uber in their operations and upgradations?

The Indian industry is growing from strength to strength, developing capabilities around digital verticals and customer segments, expanding global delivery presence across markets, and increasing focus on high value services.

The new economy is highly interconnected and interdependent, and every innovation has a domino effect. Thus, to stay globally competitive, the need of the hour for us is to invest in the future and enhance their digital capabilities. This entails a mix of reskilling, domain and platform capabilities coupled with acquisition led competencies.

Uber’s platform touches all corners of the world, and we’re building products so a rider in semi-urban city in India can access transportation as easily as global urban commuter. We’re focused on understanding riders’ unique needs, and creating features so they can take advantage of Uber’s platform in a way that fits them, therefore, we need to keep up with the technological advancements in the industry to constantly meet the needs of our consumers.

How will advanced AI-ML change the Uber experience? What elements of both have you already implemented and how?

At Uber AI-ML are very integral to the overall user experience. Uber has been successful in implementing ML to solve critical business problems and bridge the gap between the physical and digital world. We use ML for a very diverse set of applications. At Uber, deep learning, an area of artificial intelligence research, finds use in multiple applications, including improving our understanding of cities and traffic, helping compute ETAs, and in developing self-driving cars. We’ve used machine learning to optimize delivery times on Uber Eats, streamline data workflow management, and improve the customer support ticket response experience. In the areas like Labelling, Annotation Technologies, Computer Vision and NLP etc., we have huge requirement of ML talent.