This as-told-to essay originates from a detailed conversation with Lambert Liu, a practicing software engineer. The narrative has been carefully refined for both conciseness and clarity. Business Insider has formally confirmed the accuracy of Liu’s employment history as well as his academic credentials, ensuring that the account presented here is reliable.
For many individuals graduating with a degree in computer science, the decision to pursue employment within the realm of Big Tech often seems almost predetermined. The allure of companies such as Google, Meta, and Amazon is immediately apparent. They not only offer the prestige attached to working at globally recognized corporations but also provide the promise of professional stability, predictable advancement opportunities, and well-structured career trajectories. Unsurprisingly, a significant portion of my peers gravitated toward these firms, driven by the compelling mixture of security and status that they presented.
Yet, my own trajectory diverged. Instead of following the path most commonly traveled, I discovered myself increasingly aligned with another subset of students who harbored a distinct preference: seeking positions within emerging startups. The rationale was twofold. First, the chance to experience steep and accelerated learning curves by working in less defined, rapidly evolving environments appealed to me. Second, startups offered the possibility of gaining equity, a financial upside that, if the company should succeed or eventually go public, could yield substantial rewards.
This realization did not occur in isolation but rather evolved after I engaged in internships both within Big Tech and in smaller startup environments. During my sophomore and junior years, I completed two internships at Google. The initial experience was deeply satisfying. As a student, I was thrilled by both the resources available and the exposure to large-scale engineering practices. However, when returning the following year, I felt my professional development had begun to stagnate. The pace of skill acquisition slowed, and I struggled to envision a fulfilling long-term future at the company. It was during the summer following my junior year, when I transitioned to an internship with Replit—an up-and-coming AI software development startup—that my perspective solidified. At Replit, I was entrusted with leading projects that genuinely mattered, and I relished the autonomy and impact afforded to me. The contrast was striking. That decisive experience set me firmly on the path toward pursuing startup work and ultimately led to my first post-graduate role at Graphite, a company specializing in AI-driven code review.
Based on my personal journey, I would like to share several insights that may assist others aspiring to secure roles in the domain of AI startups.
**Big Tech experience can be an asset**
If your academic or early professional experience mirrors mine—consisting predominantly of internships or work within industry giants like Google or Meta—you should not worry that this precludes you from transitioning into startups. It is a mistaken belief that prior startup experience is required in order to work at one. On the contrary, experience with one of the major technology companies demonstrates an ability to master rigorous technical standards. Those internships show recruiters that you possess a foundational mastery of crucial skills such as designing robust systems, implementing thorough testing practices, writing clean code, and delivering reliable production-ready software. Although prior experience in the startup ecosystem can help by signaling comfort with ambiguity and adaptability, you can replicate much of that by dedicating time to building personal projects outside of official coursework or internships.
**Devote time to building projects**
Throughout college, I frequently engaged in passion projects during my spare time. These side ventures were not only intellectually stimulating but also crucial to refining my ability to approach unfamiliar problems. They cultivated technical skills as well as a mindset geared toward innovative problem-solving. Importantly, these projects do not need to be centered solely around artificial intelligence. In fact, using AI-based tools simply to accelerate productivity can be valuable, though limiting yourself strictly to AI work is unnecessary. Employers at AI-focused startups are primarily searching for well-rounded engineers equipped with strong analytical and technical reasoning skills. Thus, whether your projects are directly AI-related or not, what matters most is that they reveal your problem-solving style, creativity, and capacity for execution.
**The role of LeetCode preparation—necessary but evolving**
Practicing algorithmic and coding challenges on platforms such as LeetCode remains relevant, as most technical interviews continue to include sections testing these skills. However, when interviewing at AI startups, there tends to be less emphasis purely on algorithmic puzzles and far more focus on one’s ability to reason in less structured environments. Recruiters and hiring managers often evaluate whether you can grapple with ambiguity, employ product-oriented thinking, and flexibly extend your engineering approach beyond code. Since AI is increasingly automating portions of the coding process, your differentiating value lies in the way you conceptualize and execute broader problem-solving strategies. Working on personal projects naturally develops this skill: being forced to build something from the ground up encourages refinement of your own taste, perspective, and methodical approach.
**Master system design thinking**
During my interview at Graphite, I encountered system design questions for the first time. These are rarely asked of recent graduates, yet they are crucial because they reveal more than technical ability—they demonstrate how you dissect a problem, scope the requirements, and then structure an effective solution. While foundational coursework in subjects like algorithms and data science remains indispensable, deeper exposure to areas such as human-computer interaction can provide equally valuable preparation. In fact, during my time in a college HCI course, I learned not only how to theorize about design but also how to apply those principles in building tangible projects. This education helped me considerably in interviews where system design thinking was evaluated.
**Strive to be a holistic engineer**
Perhaps the most essential lesson is that thriving at a startup demands more than raw technical efficiency—it requires becoming what I consider a holistic engineer. This means embracing rapid execution, demonstrating accountability for your work, and maintaining a deep regard for the user experience behind every product. Startups operate quickly, often without the layers of bureaucracy seen in larger corporations, which makes efficiency and ownership vital. From the earliest stages of your career—even as an intern—you can begin developing this mindset. Show supervisors that you genuinely care about your craft and are consistently committed to building the best possible product. By taking ownership and acting with both speed and quality, you demonstrate that you are prepared for the demanding pace characterizing AI startups like Graphite.
Ultimately, my path illustrates that a career in AI startups is accessible to those willing to cultivate breadth as well as depth. While Big Tech can provide a strong technical foundation, side projects, creative exploration, system design thinking, and the cultivation of holistic engineering practices are what truly prepare you to thrive in the fast-paced entrepreneurial world of AI-driven innovation.
Sourse: https://www.businessinsider.com/how-to-get-job-ai-startup-google-internships-tips-tech-2025-9
