Buddhika Vimukthi Senaka Ralalage shows how disciplined, ethical leadership unites speed and stability, building transparent, fair, and efficient systems that scale responsibly while preserving human creativity.
Meta announced in April that it would raise spending levels in 2024, spending up to $10 billion for AI infrastructure. The announcement initially sent shares plunging, the company’s stock down approximately 19 %, but investors have since embraced the company’s costly push into AI. Meta’s stock price hit a record on Dec. 11, and remains up roughly 70% for the year.
Software moves at the speed of ambition. Cloud platforms deploy updates hourly, AI models rewrite their own code, and user expectations reset with every tap. Yet in that race for velocity lies a paradox: how can technology advance quickly without collapsing under its own complexity?
Within this transformation, Buddhika, a Software Engineering Manager at Meta, has become a leading example of how discipline can become a competitive advantage. His frameworks for elastic inference and system resilience illustrate how constraints can spark creativity rather than suppress it. Buddhika’s career has spanned Wish’s startup rocket ship to leadership roles at Amazon Music and now Meta, where his teams operate at a planetary scale, managing millions of daily interactions, trillions of stored posts, and the GPU clusters that drive Meta’s generativeAI expansion.
Buddhika argues that leadership in engineering isn’t about managing codebases; it’s about designing systems of people. His work shows how to build organizations that move quickly yet remain coherent, even as innovation accelerates and pressure on teams intensifies.
In this conversation, Buddhika explores how leaders can create clarity to complexity, translate technology into sustainable impact, and ensure that, even in the era of AI, human ingenuity remains at the center.
Buddhika, you’ve led transformations that measurably improved performance and profitability, from reclaiming GPUs at Meta to scaling logistics networks at Wish. Could you share one or two recent engineering changes that reflect how technical decisions ripple into business outcomes?
At Meta, the Elastic GPU Inference initiative was transformative. We repurposed over 10,000 GPUs to support AI ranking workloads without buying new hardware. This prevented millions in spending and met one hundred percent of the GPU demand for critical ranking systems. In reality, what looks like an infrastructure optimization is an organizational choice to invest in elasticity instead of expansion.
At Amazon Music, the transformation came through operational excellence. We unified student verification systems across Amazon’s subsidiaries, Music, Prime, and Podcasts, and reduced per-request costs by seventy percent. That’s not a technical breakthrough, but it freed up thousands of engineer hours annually and shortened customer turnaround times by four-fifths.
The throughline across both experiences? Speed alone doesn’t win; it endures only when systems eliminate recurring friction. The job of an engineering manager is to find those persistent drags on progress, and relentlessly process and systematize
Software delivery has become a fully distributed endeavor, spanning continents, time zones, and even cultural frameworks for collaboration. Managing such teams is often less an exercise in coordination and more one in orchestration. How do you maintain clarity and cohesion across globally dispersed engineering teams?
Distributed engineering works only when you stop pretending it doesn’t exist. Every timezone mismatch, every asynchronous decision, that’s the reality of how software at scale is built today. I focus on clarity systems rather than communication volume. That means codifying decisions in writing, prioritizing artifacts over anecdotes, and ensuring that every engineer, whether in New York or Singapore, has access to the same context.
For instance, at Meta, we shifted major infrastructure planning into documented Decision RFCs, open for asynchronous comments from any engineer. This moved us from dependence on meetings to clarity. You can’t scale people like servers, but you can scale transparency. When everyone trusts that decisions are datadriven and reversible, they start feeling collaborative, a system where good ideas can come from anywhere.
The rapid evolution of AI, cloud-native tooling, and automation often creates as many distractions as opportunities. Many organizations chase innovation for optics rather than value. How do you personally differentiate between genuine technological advancement and short-lived trend chasing?
Innovation deserves the name only when it proves its lasting value, when improvements multiply over time instead of fading after launch. I tend to judge new technologies not by what they promise, but by what constraint they actually remove. The most overlooked measure of progress is efficiency. Real innovation expands capability, reduces friction, and scales sustainably. Everything else is simply curiosity without consequence.
When a new tool appears, my first question isn’t What can it do? But what problem does it actually remove? Over time, I’ve learned that the most underrated innovation is simple efficiency. If a solution helps engineers do more with less effort and fewer barriers, and continues to work as it scales, it’s genuine progress. Everything else is just theory, with a marketing department.
Building cultures that foster trust and adaptive learning is arguably harder than scaling infrastructure. How do you approach mentorship, inclusion, and psychological safety in high-performance, remote-first teams?
I believe psychological safety isn’t about being nice; it’s about being honest in a safe way. We create spaces where engineers can challenge assumptions without fear of punishment. At Meta, I run blameless deep dives, which focus entirely on understanding failure as a system outcome, not as a people problem. Once you decouple fault from identity, creativity unlocks itself.
Mentorship is a multiplicative function, not a one-on-one transaction. I encourage peer mentorship loops, senior engineers mentoring rising ones through real project ownership rather than formal programs. It scales trust and develops self-correcting habits in teams.
Inclusion becomes natural when there is transparency in how credit and responsibility are distributed. Teams mirror the systems you design. If you design for fairness, you get innovation as a side effect.
With AI transforming engineering itself, from coding assistance to predictive decision systems, the question of responsible leadership has become urgent. What do you see as the ethical frontier for software leaders, and what principles should define the next decade of decision-making?
AI forces us to confront scale in a moral dimension. Automation multiplies both efficiency and impact; it demands that leaders discern not just what we can build, but what we should build. Responsible leadership will increasingly mean designing processes that incorporate accountability, with humans in the loop, rather than excluding them from the picture.
I see the future of leadership as a synthesis of systems thinking and ethical reasoning. Tomorrow’s engineering managers will need to understand bias propagation as deeply as they understand scaling architecture. That’s the new literacy.
In practical terms, it’s also about designing teams that use automation to amplify human creativity. For example, we used LLM-driven automation at Meta to compress a six-month engineering process into a week, freeing developers to focus on problem-solving rather than process drudgery. That’s what good AI adoption looks like: removing tedium, not replacing contribution. So, the next decade of engineering will reward those who lead with understanding limits not as obstacles, but as guides to responsible invention.
