Senior SW Engineer – AI Infrastructure & Optimization

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