Dropbox

There wasn't one thing; it was more the pattern across roles. I've built and scaled zero-to-one products, from early-stage concepts through growth inflection. At Meta, that included leading the Instagram growth team and building new messaging products at Messenger.

At Shopify, I worked on the self-serve business and led the performance marketing org with a focus on making it as easy as possible for anyone to create a Shopify store around the world. I've also spent a lot of time thinking about how product and go-to-market functions integrate (I even co-wrote the book, Hacking Growth!

). Not just how we build, but how those products are distributed, messaged and supported. That full-stack orientation matched what Dropbox needed.

I try to operate with high agency, low ego and clear thinking. I think that combination of technical fluency, go-to-market depth and a practical mindset helped me stand out.

What separates great candidates is their ability to think and act like product owners. That means connecting your work to real outcomes — customer impact, business value and system-wide improvement. Craft is the baseline; engineers who ask "Why are we solving this?

" or "How will this change behavior?" tend to stand out. It shows they're thinking beyond their lane.

The second trait I look for is agency. People who don't wait for perfect specifications, who take initiative and iterate, tend to grow faster. That said, growth doesn't just come from speed, it comes from clarity, from learning loops and from surrounding yourself with thoughtful peers.

Look for environments that support experimentation and leaders who value product sense and curiosity as much as raw execution.

I subscribe to a list of newsletters and blogs that cover a wide range of topics, helping me stay informed and gain insight into new tools, frameworks and industry best practices. Beyond reading, I actively participate in a number of company-provided training programs and take advantage of internal tools designed to facilitate continuous learning.

These programs cover everything from technical deep dives into new technologies to soft skills development, ensuring that I'm always growing both technically and professionally. I also attend learning sessions organized by my team and wider company, where we share knowledge on what we're working on, discuss challenges and explore new solutions together.

In addition, collaborating on projects and seeking feedback from colleagues has been a great way to refine my skills, gain perspective and learn from others who bring different expertise and viewpoints.

Continuous learning is deeply ingrained in Dropbox culture. Engineers are encouraged to take ownership of their professional growth through a wide range of learning programs. At the team level, we host regular knowledge-sharing tech talks where members present new insights, best practices and tools.

Mentorship is also a key part of our culture, both through formal mentorship programs and informal peer mentoring. Engineers regularly collaborate on complex problems, exchanging ideas and approaches to drive collective learning and innovation.

As a technical lead for Dropbox Core, the team behind our file, sync, and share product, I help ensure learning is part of how we work every day. Each summer, we pause our regular projects for Hack Week: five days dedicated to exploring bold ideas, collaborating across teams and developing new skills.

This spirit of innovation continues year-round through self-service training, from quick skills sessions like strategy planning to intensive programs such as the AI Academy and Ascent leadership program. We also invest in peer-to-peer learning. Our mentor-matching program connects engaged mentees with experienced mentors, fostering growth on both sides.

And we encourage creative knowledge-sharing, from tech talks to coding challenges, that bring engineers together across the company. By embedding continuous learning into our culture, Core keeps growing the skills that will shape Dropbox's future.

Learning and innovation deliver clear business value. Hack Week has produced product features used by millions. Many promoted engineers are recognized for sharing their work in forums like our annual offsite, where front-end engineers exchange technical insights, which has improved both the speed and quality of our engineering.

Targeted programs like the AI Academy have helped expand the use of AI tools across the company, contributing to measurable productivity improvements in many teams. But just as importantly, creating a strong learning culture boosts engagement, happiness and retention, a cultural impact that is every bit as significant as the tangible gains in product quality, speed and productivity.

Having a role in creating this culture — and sharing in the growth it creates for everyone — is a key reason I continue to be excited to work at Dropbox.

Like anything, you start small and build up. A key point is recognizing that sharing is just as critical as consuming when creating a learning culture. I recommend taking a course every few months, but I've often learned just as much from peers who shared a hidden skill with me on Slack.

The next step, the one that truly builds culture, is encouraging that peer to add a bit of polish to their write-up and share it with the whole team. It seems small, but people notice when a colleague takes those few extra minutes to make a recommendation.

That can quickly grow into a #tips-and-tricks Slack channel or an internal Confluence page, our shared knowledge base, filled with key insights and shortcuts. The best of these can grow into recurring formats like weekly tech talks or brown-bag lunches — and, if momentum builds, even an annual offsite for front-end engineers.

The key to accelerating this cycle is consistent support and recognition at every stage, with leaders celebrating not only the big wins but, most importantly, the small sparks that get everything started.

I try to structure my day pretty similar to how I did going into the office. I don't do anything particular to mimic a commute — that was the worst part of my day! — but I am still exercising most mornings.

I often take small walks during the day, but for me keeping work time focused on work helps with my productivity. Working from home sometimes means there are more opportunities for distractions, so I typically assign myself one or two tasks daily that need to get done.

Outside of meetings, those are my priorities, and I like to set myself a timer to dig into them and focus before allowing myself to get distracted on other projects. This is super helpful for getting myself into a flow and remaining productive. I almost always do my "most annoying" task first to get it out of the way because I work best when settled in without big disruptions.

My team at Dropbox generally does a great job keeping standing meetings within my morning hours of about 9 a.m to 1 p.m.

This means that afternoons are almost always free for either heads-down work or deep collaboration! One of the tricky aspects about remote work is needing to be proactive in reaching out for more casual brainstorms and collabs. I try to utilize quick Zoom syncs as much as possible and often spend one-on-ones simply brainstorming and talking out loud even if we don't have a super-focused agenda topic for the day.

Part of my role is to help shape team strategy through data and insights. Since I don't have as much casual conversation time, one technique I deploy is to create a personal insights channel and invite several people to it. This is where I share in-progress work, random insights I find that might not be fully packaged or ideas I have.

I have found this super helpful in driving conversations and inspiring future work as colleagues can comment on their own time and keep up with my progress!

In 2025, I am most excited to work on the new AI feature for Dropbox Dash. This project is particularly compelling because it presents an opportunity to integrate some of the most cutting-edge AI capabilities into real-world applications, directly impacting how users interact with their data.

The ability to merge state-of-the-art AI advancements with Dropbox Dash is both exciting and challenging.

The roadmap for this project involves several key phases. Initially, the focus will be on defining the AI feature's scope and identifying the most relevant use cases for Dropbox Dash users. Next, we will focus on prototyping, iterating and testing the AI capabilities to ensure they integrate smoothly with the platform.

As we move into the implementation phase, I'll collaborate closely with engineers, product managers and UX/UI designers from different teams to ensure the feature aligns with both technical requirements and user needs. One of the key challenges I anticipate is ensuring that the AI remains simple and intuitive while leveraging its full potential.

I plan to overcome this by maintaining a strong focus on user testing and feedback to iterate quickly and fine-tune the feature. Additionally, addressing potential scalability issues early will be critical to smooth deployment.

My background in machine learning research during my PhD, combined with my experience as a machine learning engineer at Dropbox during my three internships, has uniquely prepared me for this project. Throughout my internships, I worked on real-world ML applications, honing my ability to build, optimize and scale machine learning models effectively.

These experiences, along with my deep understanding of AI and its practical applications, have equipped me with the technical skills needed to integrate AI into Dropbox Dash. I also gained valuable experience in cross-functional collaboration, which will be crucial for working efficiently with other teams.

Through this project, I hope to deepen my expertise in deploying AI at scale and learn how to evaluate and fine-tune AI solutions for enterprise use cases.

Building Dropbox Dash taught us that releasing fast only works when evaluation is built in. We treat every change, from prompts to retrievers to model settings, with the same rigor as production code. Each pull request runs about 150 canonical queries, judged automatically in under ten minutes.

Metrics like source F1 (≥ 0.85) and latency (p95 ≤ 5 s) keep us accountable. This structure enables fast, confident releases across a platform trusted by more than 700 million users.

At Dropbox, quality is measurable, versioned, and enforced. Every change is scored across Boolean gates like "Citations present?", scalar budgets such as source F1 and latency, and rubric scores for tone, clarity, and formatting.

We use LLMs as judges, guided by calibrated rubrics that check factual accuracy and context alignment. The results feed shared dashboards so quality stays visible, repeatable, and reliable across Dropbox's global infrastructure.