Tavus is a research lab pioneering human computing. We’re building AI Humans: a new interface that closes the gap between people and machines, free from the friction of today’s systems. Our real-time human simulation models let machines see, hear, respond, and even look real—enabling meaningful, face-to-face conversations. AI Humans combine the emotional intelligence of humans with the reach and reliability of machines, making them capable, trusted agents available 24/7, in every language, on our terms.
Imagine a therapist anyone can afford. A personal trainer that adapts to your schedule. A fleet of medical assistants that can give every patient the attention they need. With Tavus, individuals, enterprises, and developers can all build AI Humans to connect, understand, and act with empathy at scale.
We’re a Series B company backed by world-class investors including Sequoia Capital, Y Combinator, and Scale Venture Partners.
Be part of shaping a future where humans and machines truly understand each other.
We're hiring a Senior Software Engineer (Infrastructure) to own the systems behind CVI, our real-time conversational product. Every live conversation between a person and a PAL runs on infrastructure your team owns. You'll take goals like uptime, latency, and cost and chase them wherever they lead, including into backend services and product code.
CVI's inference deployments. The GPU infrastructure serving live conversations across multiple providers and regions. You'll join as an early senior member of a growing infra team, working on projects like tuning the newest GPU generations and cutting cold-start and model load times so users wait less.
Expanding our GPU footprint. You'll bring on new providers and regions, stand up clusters on EKS, and build the routing, scheduling, and throughput needed for fast weight loading.
Uptime. You'll be one of the people pushing our uptime bar higher, along with the security and SOC2 work that keeps our infrastructure trustworthy.
Fix what you find. When you see a problem, you have the trust and the mandate to fix it or flag it. Reworking our deploy pipeline so shipping is fast and boring is exactly the kind of thing you'd take on.
Multi-provider, multi-region inference infrastructure: routes live conversations across GPU providers and regions, so one provider's outage never becomes a user's problem
CUDA optimizations for Phoenix, our video rendering model: doubled the frame rate by tracing and optimizing hot paths with our researchers
Parallel conversations on a single GPU: several live conversations sharing one card, multiplying what the fleet can serve
You own outcomes. You don't stop where "infrastructure" ends. If the fix lives in backend code or the CVI stack, you dive in, and you don't wait for a ticket to do it.
You're energized by unfamiliar problems. If the next thing that matters is standing up a training deployment you've never touched, you jump in and learn on the fly.
You adapt as priorities evolve. In a space moving this fast, the most important thing to build can change as we learn. When it does, you adjust course without losing momentum.
You care about this problem. Keeping large-scale, real-time systems fast and reliable is something you think about unprompted.
Hands-on GPU inference experience. You've deployed and optimized inference workloads on GPUs and know what it takes to build reliable systems on top of GPU cloud providers.
Kubernetes and EKS depth, including routing and scheduling. You're comfortable designing how work gets placed across a fleet, and writing the services that make it happen.
Deep AWS experience. You're at home spinning up new services and turning them into simple, repeatable processes others can build on.
A senior track record of ownership. You've set technical direction, made decisions others built on, and carried ambiguous work over the finish line. You explain complex ideas clearly, to engineers and non-engineers alike.
Experience with GCP
Experience with video streaming infrastructure
Experience with training infrastructure or LLM serving
Experience with SOC2 or security compliance
If you don't check every box but this sounds like the work you want to be doing, apply anyway.
Design and operate large-scale Linux-based AI infrastructure, from provisioning and cluster bring-up to Kubernetes deployment, GPU performance optimization, and reliability for customer workloads.
Build observability, automation, and reliability tooling for distributed infrastructure, turning high-volume telemetry into actionable insight and translating SRE goals into scalable systems that improve operational performance.
Build and operate infrastructure software that connects services and routes traffic reliably, improving the scalability, resilience, and observability of distributed systems while owning production reliability.
Design, build, and operate reliable, large-scale AI cloud infrastructure, improving performance, capacity, automation, and service health across multi-GPU and distributed systems.
Build and operate AWS infrastructure and MLOps platforms that help data scientists bring models into production, while setting standards for secure, reliable systems and shaping the platform roadmap.
