What You’ll Do:

• Architect the Future of AI Infrastructure: You will design, build, and own the end-to-end platform that supports the entire lifecycle of our ML models—from massive-scale distributed training to ultra-low-latency, highly-available inference.
• Optimize and Serve Cutting-Edge Models: You'll implement and scale sophisticated inference stacks for LLMs using frameworks like vLLM, TensorRT-LLM, or SGLang. You’ll solve complex challenges in throughput, latency, token streaming, and automated scaling to deliver a seamless user experience.
• Empower AI Innovation: You will act as a strategic partner to our AI Research and Data Science teams. You’ll create a seamless developer experience that accelerates their ability to experiment, fine-tune, and deploy groundbreaking models with velocity and confidence.
• Automate Everything: You'll develop robust CI/CD/CT (Continuous Training) pipelines using tools like Argo Workflows, ArgoCD, and GitHub Actions to automate model validation, deployment, and lifecycle management, ensuring our systems are both agile and rock-solid.

What are we looking for

• Experience: 5+ years in infrastructure or software engineering, with at least 2+ years laser-focused on MLOps or ML infrastructure for large-scale distributed systems.
• Education: A Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Kubernetes & Cloud Native Expertise: Deep, hands-on expertise with Kubernetes in production. You are fluent in the cloud-native ecosystem, including Helm, ArgoCD, and Argo Workflows.
• GPU & Cloud Mastery: Optimize the platform’s performance and scalability, considering factors such as GPU resource utilization, data ingestion, model training, and deployment.
• Modern LLM Serving Experience: Hands-on experience with modern LLM inference serving frameworks (e.g., vLLM, SGLang, Triton Inference Server, Ray Serve). You understand the unique challenges of serving generative models.
• Strong Coder: Strong programming proficiency in Python or Go, with experience using ML frameworks like PyTorch, Jax, TensorFlow.
• Observability Mindset: A passion for building observable and resilient systems using modern monitoring tools (e.g., Prometheus, Grafana, OpenTelemetry).

We would love to see:

• Deep performance optimization skills, including writing custom inference kernels in CUDA or Triton to accelerate model performance beyond what off-the-shelf frameworks provide.
• Experience with model optimization techniques like quantization, distillation, and speculative decoding.
• Exposure to training and serving multi-modal models (e.g., text-to-image, vision-language).
• Knowledge of AI safety and evaluation frameworks for monitoring model performance for things like bias, toxicity, and hallucinations.