Runpod

Runpod is high-agency, high-ownership, and right on the edge of AI with tons of compute. In a short time I've benchmarked NVIDIA Blackwell, shipped with the newest open-source models, and helped an ~80-person team deliver features to 450,000+ devs. The learning rate and chance to build on the edge is why I'm here.

We have a flexible, unlimited PTO policy that empowers us to recharge without guilt, supported by a culture of trust and accountability. Runpod has experimented with new initiatives, like piloting "Summer Fridays," where we collectively logged off early every other week to make the most of the season.

As a remote-first company, we also have the option to gather in regional offices, like Industrious in Seattle, that Runpod sponsors, giving us space to connect without enforcing an in-person mandate. Runpod invests directly in us, too: Every employee receives a $500 yearly stipend for education and training and $300 for health and wellness.

Early on, I was supported in taking time off for my wedding just months after joining, which allowed me to fully unplug and focus on celebrating with family. I've also used the education stipend to enroll in a visual thinking workshop, something not directly tied to programming but invaluable for my work as an engineer.

Visual thinking helps me distill complex architectures into clear, shareable visuals, making it easier to process, understand and communicate ideas. Being encouraged to pursue something personally meaningful yet professionally relevant reinforced that Runpod values growth in all its forms, not just job-specific training.

Runpod's culture is built on valuing people as whole individuals, not just employees. One of the most unique parts of our culture is that we have a dedicated Culture Champion, Ty, whose role is to strengthen connection and creativity across the company.

Each week, he curates a Spotify playlist featuring picks from different employees, giving everyone a chance to share a bit of themselves. Ty also happens to be a master at producing musical performances and has even worked on performances during our all-hands meetings featuring Zhen, our CEO, who's been taking vocal lessons.

It's moments like these, where creativity and vulnerability are celebrated, that make Runpod's culture feel authentic, connected and fun.

Right now, I am focused on a novel project at RunPod called MultiNode. With the rise of massive AI models, developers have turned to distributed graphics processing unit clusters for their training and fine-tuning needs. Bridging these clusters results in a high-performance network, starting at a 200 gigabits per second baseline with the potential to reach multi-terabit bandwidths, enabling GPUs to communicate and work together seamlessly.

The key challenge when developing a public cloud is isolation, as each customer requires a totally sealed environment. One workload cannot interfere with another, even on a networking level. I have been working with a small team to develop a hardware accelerated isolation engine, which enables us to create a software-defined overlay network scalable to millions of customers.

This project has bridged my personal interests in scientific computing and high-performance computing. A favorite saying of mine is that "a supercomputer is a device for turning compute-bound problems into input/output-bound problems." This paradigm is all the more relevant in the age of AI.

Bringing together my experience with general purpose computing on graphics processing units and learning how industrial-grade networking works deep under the hood has been an amazing experience.

I'm most excited about going to market and launching this at a massive scale, as we're collaborating with the entire organization. Sales, marketing, hardware supply, leadership — everyone is involved and supportive in making this project a success. Seeing beta customers try out this feature on large, highly networked Nvidia H100 clusters and run their demanding workloads at scale is nothing short of thrilling.

The biggest challenge I foresee is scaling to meet demand; these networks are complex and expensive, and ensuring both cluster quality and quantity will define our success.

My background spans AI, machine learning, mathematics, real-time LiDAR processing pipelines and compute unified device architecture accelerated scientific computing. At RunPod, we're building a public cloud for over 100,000 customers and 21 datacenters across the world.

This means that every engineer within the team has enormous ownership and impact. There's incredible latitude to build the best products with the best technologies, and we're constantly pushing what's possible. Each engineer owns a major scope of impact, and these projects are critical to the success of the company.

I personally look forward to diving deeper into the performance engineering portion of my background and applying it to RunPod's products and vision — bridging AI and cloud infrastructure.

It's our continued developer adoption with roughly 500,000 using the platform to bring their AI ideas to life. This velocity has held strong and best demonstrates that the thesis from the beginning has held true: Make it easier for AI developers to build and launch AI systems that scale.

We are built by developers, for developers. We see this in the breadth of use cases deployed on Runpod; nearly every major dev tool, both new and mature, and both large and small models have been used in conjunction on Runpod to build AI systems that scale.

From training and fine-tuning to inference and production, developers continue to push the limits of AI with Runpod. However, what is perhaps the most invigorating is that we see the developers ranging from hobbyists to startups to large enterprises, and have been able to meet the needs across the spectrum.

Being close to the people and ideas that go from a figment of imagination to a proof of concept and, ultimately, production is not only about competition but the right place to be for the maturity of the market and innovation to come. We remain committed to focusing on the people and communities that make AI "real.

".

We are excited about our increased ability to support larger organizations both from a product and support/services perspective. This readiness and increased capabilities extend value to our builders and developers. While many are still looking for pay-as-you-go as they establish/validate their ideas, there is a substantial cohort looking at Runpod as an ongoing partnership and critical cornerstone for their AI development stack.

They are relying on us to help them not only power their AI systems but also serve as the connectivity layer to that required "compute" and solve complex projects together. We are becoming engrained in their dev lifecycle and business strategy overall.

The indicators we see and continue to focus on are an increase in the volume of models and systems used on the platform, and requests to be extended, as the software, on top of infrastructure, regardless of location/provider.