Senior Member of Technical Staff: ML Systems and Infrastructure

Overview

Senior Member of Technical Staff for ML Systems and Infrastructure, responsible for designing and operating end-to-end ML platform infrastructure, from distributed training to low-latency inference.

What you'll do

• Design, build, and own an end-to-end platform supporting the full ML model lifecycle.
• Implement and scale inference stacks for LLMs to improve throughput, latency, token streaming, and automated scaling.
• Partner with AI Research and Data Science teams to accelerate experimenting, fine-tuning, and deployment.
• Develop CI/CD and continuous training pipelines for model validation, deployment, and lifecycle management.
• Ensure the systems are agile and highly available.

What you'll need

• 5+ years of infrastructure or software engineering, including 2+ years in MLOps or ML infrastructure for large-scale distributed systems.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Deep hands-on Kubernetes production expertise.
• Cloud-native ecosystem experience including Helm, ArgoCD, and Argo Workflows.
• Hands-on experience optimizing performance and scalability including GPU utilization.
• Experience with modern LLM inference serving frameworks (e.g., vLLM, SGLang, Triton Inference Server, Ray Serve).
• Strong programming proficiency in Python or Go; experience with ML frameworks like PyTorch, Jax, or TensorFlow.
• Observability mindset using monitoring tools such as Prometheus, Grafana, and OpenTelemetry.

Details

• Location: Bangalore, India.