In this insightful interview, we sit down with Tejas Chopra, Senior Engineer at Netflix and Co-Founder of GoEB1. With a career spanning major tech companies like Netflix, Box, and Apple, Tejas offers a deep dive into the challenges and innovations in scalable data systems, AI, and automation. He also shares his vision for sustainable AI practices and emerging tech trends. Discover how his technical expertise fuels both his engineering work and his mission to support immigrant communities through GoEB1. This conversation promises a rich exploration of current and future tech landscapes.
As a Senior Engineer at Netflix, you are deeply involved in building a distributed, scalable data infrastructure for recommendations. Could you share the most significant challenge you’ve encountered in developing this system, and how your team overcame it?
As a Senior Engineer for the Machine Learning Platform at Netflix, I’ve been working on architecting feature stores for Netflix recommendations. Previously, I worked on architecting Netflix Drive – a cloud file system that allows artists to collaborate and share their assets. One of the challenges we faced with COVID-19 was allowing remote work for content creation. The existing technology and tools were fragmented and expensive. So, we had to design and architect a home-grown cloud file system that is scalable, secure, and efficient. We’ve implemented a hybrid storage approach, which allows us to balance performance and cost-effectiveness. By leveraging cloud technologies and implementing smart data placement strategies, we’ve been able to significantly reduce storage costs, while maintaining the high performance necessary for content creation.
In your role as Co-Founder of GoEB1, you’re providing thought leadership for immigrants. How do you leverage your technical expertise to empower and support immigrant communities through this platform?
As the Co-Founder of GoEB1, which is the world’s first and only thought leadership platform for immigrants, I have partnered with Mahima Sharma, who is a leader in the HR space and a certified coach, to leverage my technical expertise and experience as an EB1A (Einstein) visa recipient to empower and support immigrant communities. Our platform focuses on sharing knowledge, experiences, and strategies for navigating the complex immigration process, particularly for highly skilled professionals in tech and other fields.
We utilize technology to create a user-friendly platform that connects immigrants with resources, mentors, and opportunities. My background in cloud computing, microservices, and large-scale systems helps ensure that our platform is scalable, secure, and accessible to users worldwide. Additionally, we incorporate AI and machine learning technologies to personalize content and recommendations, helping users find the most relevant information for their specific immigration journey.
Given your diverse experience across leading tech companies like Box, Apple, and Netflix, what key lessons have you learned about the role of AI and automation in driving business success, and how can emerging startups harness these technologies effectively?
Through my experiences at companies like Netflix, Box, and others, I’ve learned that leveraging ML and AI for automation is crucial for scaling operations, improving efficiency, and driving innovation. At Box, we leveraged ML for smart data placement and lifecycle policies, which significantly reduced costs and improved service availability. At Netflix, our ML platform is central to delivering personalized experiences at a global scale.
For emerging startups, the key is to identify specific, high-impact areas where AI can solve real problems or create significant value. Start with well-defined use cases and focus on data quality and infrastructure. It’s also crucial to build a culture that embraces AI and automation, investing in skills development and cross-functional collaboration.
Startups should also be mindful of the ethical implications and potential biases in AI systems. Implementing responsible AI practices from the outset can help build trust with users and prevent future challenges.
You have spoken extensively on the impact of AI on the environment. In what ways do you believe AI can contribute to sustainable development, and what ethical considerations should guide its implementation?
Yes, I have given a couple of TEDx talks on the topic of Carbon footprint of software in general, and AI specifically. With the growth in usage of AI, it is imperative that we understand its implications on the environment and identify ways to reduce the carbon footprint of training AI models and running inference.
AI can significantly contribute to sustainable development by optimizing resource usage, predicting environmental changes, and supporting renewable energy integration. For instance, in my work with storage infrastructure, we’ve used AI to optimize data placement and lifecycle management, which not only reduces costs but also minimizes energy consumption.
Ethical considerations should include:
1. Energy efficiency: Ensuring AI systems are designed to minimize their carbon footprint.
2. Transparency: Making the environmental impact of AI systems measurable and reportable.
3. Fairness: Ensuring that the benefits of AI-driven sustainability efforts are distributed equitably.
4. Long-term impact assessment: Considering both immediate and long-term environmental effects of AI deployments.
As an Angel investor and startup advisor, what trends are you currently seeing in the AI and machine learning space that excite you, and what advice would you give to new entrepreneurs entering this field?
As an Angel investor and startup advisor, I’m particularly excited about advancements in federated learning, edge AI, and AI-driven automation in various industries. The integration of AI with other emerging technologies like blockchain and IoT also presents interesting opportunities.
My advice to new entrepreneurs in this field would be:
1. Focus on solving real-world problems: Identify specific industry pain points where AI can make a significant impact.
2. Prioritize data strategy: Develop a robust approach to data collection, management, and governance.
3. Build for scalability: Design your AI systems with growth in mind, leveraging cloud technologies and microservices architecture.
4. Embrace ethical AI: Incorporate responsible AI practices from the start to build trust and mitigate risks.
5. Stay adaptable: The AI field is rapidly evolving, so be prepared to pivot and adapt your strategies as new technologies emerge.
Having been recognized as a Tech 40 under 40 Award winner and a 2x TEDx speaker, how do you balance your technical contributions with your leadership and public speaking roles, and what drives you to excel in both?
Balancing technical contributions with leadership and public speaking roles requires careful time management and a commitment to continuous learning. I strive to stay deeply involved in technical work, as evidenced by my role as a Senior Engineer at Netflix, while also taking on leadership responsibilities and sharing knowledge through speaking engagements.
What drives me to excel in both areas is the belief that technical expertise and the ability to communicate complex ideas are equally important in driving innovation and inspiring others. My experience as an Adjunct Professor of Software Development at the University of Advancing Technology helps me bridge the gap between technical concepts and their practical applications.
I’m motivated by the opportunity to contribute to cutting-edge technologies while also mentoring and inspiring the next generation of technologists. This dual focus allows me to stay current with technical advancements while developing the leadership skills necessary to drive broader impact in the tech industry.
In your opinion, what will be the next major shift in AI technology that businesses should prepare for, and how can companies strategically position themselves to take advantage of these changes?
Based on my experience in machine learning platforms and cloud technologies, I believe the next major shift in AI technology will likely involve the further democratization of AI capabilities, making advanced AI tools more accessible to businesses of all sizes. We may also see significant advancements in multi-modal AI systems that can process and generate various types of data (text, image, video, audio) seamlessly.
Companies can strategically position themselves by:
1. Investing in robust data infrastructure that can handle diverse data types at scale.
2. Developing a culture of AI literacy across all levels of the organization.
3. Exploring hybrid AI models that combine cloud-based and edge computing capabilities.
4. Focusing on ethical AI practices and transparency to build trust with customers and stakeholders.
5. Staying agile and ready to adapt to new AI paradigms as they emerge.
As an Adjunct Professor at the University of Advancing Technology, how do you incorporate your real-world engineering experiences into your teaching, and what do you hope to instill in the next generation of software developers?
As an Adjunct Professor teaching Software Development at the University of Advancing Technology, I incorporate my real-world engineering experiences by bringing practical case studies and current industry challenges into the classroom. I often draw from my work in the industry to provide students with insights into how theoretical concepts apply in real-world scenarios.
I hope to instill in the next generation of software developers:
1. A problem-solving mindset that goes beyond just coding.
2. An understanding of scalability and performance considerations in large-scale systems.
3. The importance of staying current with emerging technologies and industry trends.
4. Ethical considerations in software development, especially related to AI and data privacy.
5. The value of effective communication and collaboration in tech teams.
By bridging academic concepts with industry realities, I aim to prepare students for the dynamic and challenging world of professional software development. In order to help students learn about systems design, ace their interviews, and build scalable systems, I have also co-authored a book on building scalable systems.
With your involvement in advisory boards and panels, such as the Future of Memory & Storage Summit, what emerging technologies or concepts are you particularly interested in, and how do you see them shaping the future of computing?
As a member of the Advisory Board for the Future of Memory & Storage Summit and given my background in storage infrastructure at companies like Netflix and Box, I’m particularly interested in emerging technologies related to data storage and processing. Some areas of interest include:
1. Next-generation non-volatile memory technologies that could revolutionize data access speeds and storage density.
2. Advancements in software-defined storage and disaggregated storage architectures.
3. The integration of AI/ML with storage systems for intelligent data management and predictive maintenance.
4. Edge computing and its implications for distributed storage systems.
5. Quantum computing and its potential impact on data processing and cryptography.
These technologies have the potential to dramatically reshape computing by enabling faster data access, more efficient resource utilization, and new paradigms for distributed computing. They could lead to more powerful and energy-efficient systems, capable of processing vast amounts of data in real-time, which is crucial for advancing AI, IoT, and other data-intensive applications.
As computing continues to evolve, I believe we’ll see a closer integration of storage, memory, and processing capabilities, blurring the traditional boundaries between these components and enabling more flexible and efficient computing architectures.
