<p>In this episode of <em>Tech Talks</em>, Mahima Singh interviews Siddharth Singh, a published AI researcher, engineer and MS Computer Science Candidate at Stony Brook University. Siddharth shares insights on bridging research and engineering in real-world AI systems. He highlights the fundamental difference between researchers and engineers—where researchers focus on truth and rigor, engineers focus on building reliable systems under real-world constraints—and emphasizes the importance of combining both mindsets to create impactful AI solutions.</p><p><br></p><p>Siddharth discusses how to identify meaningful research problems, design robust evaluation frameworks, and navigate the transition from research to production. He explains that real-world AI systems must account for constraints like latency, compute, and unpredictable environments, making system design—not just model performance—critical. He also highlights common failure modes in AI, including distribution shifts, metric misalignment, and human behavior adaptation.</p><p><br></p><p>In this conversation, Siddharth shares practical guidance for working across research, engineering, and product roles. He explains how strong product managers manage uncertainty, translate business problems into precise technical questions, and avoid premature assumptions. He also advises students to build technical literacy, understand the gap between research and deployment, and develop the ability to frame clear, actionable problems. The discussion concludes with insights on experimentation, iteration, and the critical role of human judgment in building reliable AI systems.</p>