Senior SW Engineer – AI Infrastructure & Optimization

Description We are looking for a Senior Software Engineer to help build and optimize large-scale, high-performance GenAI infrastructure and inference systems on Kubernetes. As AI workloads increasingly move toward Kubernetes-native infrastructure, we are building systems that support distributed inference, performance optimization, reliability, observability, and production-grade deployment at scale. This role is ideal for an engineer who can reason deeply about systems, performance, tradeoffs, and reliability, and who is comfortable owning difficult technical decisions end-to-end. You will work across inference serving, distributed systems, optimization, and Kubernetes-native AI infrastructure. What You’ll Do Build and optimize high-performance Kubernetes-native GenAI inference systems Work with modern inference stacks such as vLLM, SGLang, TensorRT-LLM, and related tooling Work with Kubernetes-native distributed LLM inference frameworks such as llm-d and NVIDIA Dynamo Design and implement optimization algorithms and performance improvements Improve reliability, observability, deployment, and operational maturity of AI systems Make architectural decisions and take ownership of technical outcomes Collaborate with a small, senior engineering team focused on performance and production quality Requirements Required Qualifications Minimum 5 years of experience as a Software Engineer, with strong software engineering and system design skills. Programming experience in Go and Python Hands-on experience with the Kubernetes ecosystem, including Operators, service meshes, GitOps, Gateway API, and OpenTelemetry Experience with cloud platforms Strong understanding of optimization algorithms and performance engineering Ability to independently drive technical initiatives from concept to production Strong systems thinking and debugging skills Comfort operating in environments with high autonomy and responsibility Nice to Have Experience with modern LLM inference frameworks such as vLLM, SGLang, or TensorRT-LLM Experience with distributed LLM inference frameworks such as llm-d or NVIDIA Dynamo Contributions to open-source Kubernetes or ML infrastructure projects GPU performance optimization and profiling experience Familiarity with CUDA, NCCL, or Triton kernels Experience running GenAI systems at scale in production