Ankit Jain: Transforming Uber and Meta with AI engineering

Experienced Machine Learning Engineer Ankit Jain shares his experiences working on AI solutions for companies such as Uber and Meta

Tell me about Meta in your own words, can you explain what it’s like to work there?

Working for Meta, I find my enjoyment from the people and the culture. I am fortunate to work with some of the smartest people I know. The second element is the culture Meta has cultivated. You have a lot of autonomy in how you work and what you work on at times, which I think is a great way to treat your employees. Meta trusts us to complete work that will benefit the company and complement our skillset.

It's also a very engineering focused culture. Engineers are able to own the problems end-to-end. The final thing about Meta that I find incredibly exciting is its position at the forefront of technology. All the cutting-edge technology you see in the current world is being applied at Meta in some way, and it’s great to be a part of that.

What is it about technology that excites you? Why did you decide to work in this field?

I think the biggest step change in humanity has always come through some technological transformation, and that is what drives me to succeed in this field. One of the things that is evident is that the pandemic has really accelerated the digital revolution. It has shown that technology is invaluable and crucial to our daily lives. Without technology, we wouldn’t have been able to connect with people virtually, which would have been a lot harder for people to navigate. So, in that sense, I believe technology is the biggest driver of human evolution and I love being part of that. 

Talk me through your work with Uber AI – how did it transform the company’s operations, and what role did you play in the implementation of AI technology?

I joined when Uber just started its AI team. The company was betting heavily on AI to transform its business, like so many others. Uber was in a great position because it has lots of data to work with. Here, I built two solutions utilising AI that were successfully deployed in the company.

One was for Uber Eats – the dish recommendation systems that you see on the app. I led this project, and we developed an algorithm to create this recommendation service for each individual on the platform. The second problem I worked to resolve was fraud detection at Uber. There was an issue with fraud on the platform, particularly for Uber trips, and to combat this, we deployed a solution to detect fraudulent trips using graph AI techniques. These two projects definitely impacted the business in a meaningful way. 

Have you experienced any significant challenges, and if so, how did you overcome them?

There are always challenges when it comes to breaking into the technology industry. I decided to learn about some of the newer technologies by myself on the side, rather than relying on on-the-job learning. I always try to differentiate myself by learning some of the cutting-edge technology and developing some projects around it.

The second challenge I feel is that mindsets change. As a technologist, you are always enamoured by the latest and greatest technology that you want to apply in your projects or in your company. It is important for technologists in business to lead with the business perspective and then think of where the technology will fit to be the most appropriate for success. I went through some painful transitions in the course of my career to think through that lens. Ultimately, this has transformed me into a better problem solver.

In what ways do you contribute to the field outside of your role?

I love teaching. I teach AI and machine learning at various boot camps to empower the next generation of technologists. I initially thought that it could be a great way to give back to people but I found that I learned a lot myself in the process. I also give talks on cutting-edge technology at top-tier conferences and advise and mentor startups.

How do you hope to further your skills in the future?

The AI field is constantly changing. A few years ago, there was a trend on big data, then a trend on AI core algorithms – but now the trend is around machine learning operations. The narrative has changed from just pure research and algorithms to considering how you deploy the technology at scale and in an effective manner.  

I'm trying to gain a deeper understanding of the latest indigenous tools out there and the technologies in machine learning. With this, I hope I will be ready to deploy these technologies at scale in my company or help out other companies in deploying this.

I want to keep up with the new trends coming in AI. Be it responsible AI, be it explainable AI, or its on-device machine learning.


Featured Articles

How Retrieval Augmented Generation (RAG) Enhances Gen AI

RAG is a technique that promises to improve the way Gen AI fetches answers and provide business with a more reliable use case for client-facing uses

Synechron’s Prag Jaodekar on the UK's AI Regulation Journey

We hear from Prag Jaodekar, Technology Director at Synechron, about how the EU AI Act will impact business innovation and how they can best use AI safely

LGBTQ+ in AI: Vivienne Ming and the Human Power of AI

This Pride Month, we honour AI expert Vivienne Ming whose ideas on machine learning continue to expand human capacity and unlock future opportunities

Samsung’s AI-Era Vision Coincides With its New Chip Tech

AI Strategy

SolarWinds Report Finds Limited Business Confidence in AI

Data & Analytics

Apple & OpenAI: Elon Musk Threatens Apple Device Ban

AI Strategy