Senior Site Reliability Engineer (GPU & ML Infrastructure)

What you'll do

• Build and operate scalable Ray clusters running on Kubernetes
• Develop reliable self-service distributed computing platforms for ML workloads
• Improve provisioning, observability, reliability, and operational efficiency of ray-as-a-service environments
• Operate and optimize large-scale inference platforms using NVIDIA Triton Inference Server
• Improve latency, throughput, scalability, and GPU utilization for deep learning inference workloads
• Collaborate closely with ML engineers, data scientists, and infrastructure teams to deliver reliable, production-grade ML platforms

Requirements

• 5+ years of experience in backend engineering, Site Reliability Engineering, or platform engineering roles focused on distributed systems
• Strong experience with Kubernetes, including workload scheduling, dynamic provisioning, and custom controllers/operators
• Hands-on experience running or optimizing GPU-based workloads in production, ideally for ML training or inference systems
• Strong software engineering skills in C#, Python, Go, or similar languages, with a focus on building reliable distributed systems
• Experience building or operating production-grade infrastructure with strong requirements around performance, scalability, and reliability
• Strong interest in automation, observability, and designing systems that scale efficiently under high load
• Bonus: Experience with distributed ML frameworks such as Ray or similar systems
• Bonus: Familiarity with inference serving stacks such as NVIDIA Triton or TensorRT
• Bonus: Experience with GPU scheduling, resource management, or multi-tenant GPU platforms
• Bonus: Exposure to cloud-native GPU orchestration (GKE, EKS, or on-prem Kubernetes GPU clusters)

Tech stack

Benefits