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Odyssey · 1 month ago

Member of Technical Staff, Infrastructure Engineer

Santa Clara, CA

Full-time

Hybrid

Mid, Senior Level

Odyssey is an AI lab pioneering general-purpose world models that will power the next generation of various applications. They are seeking an Infrastructure Engineer to develop and operate their low-latency model inference platform, scale core data processing infrastructure, and design GPU-based training clusters for deep learning.

Artificial Intelligence (AI)Graphic DesignVirtual Reality

Responsibilities

Develop and operate our low-latency model inference platform, ensuring high availability, scaling, and efficient resource utilization for Odyssey’s products

Engineer and scale our core data processing infrastructure (e.g., Flyte, Ray with k8s) to handle petabyte-scale datasets

Design, build, and maintain our large-scale, GPU-based training clusters for deep learning, focusing on high throughput and reliability

Automate infrastructure provisioning, configuration, monitoring, and alerting using Infrastructure as Code (IaC) principles

Drive performance tuning, cost optimization, and reliability improvements across the entire stack

Collaborate closely with researchers and product developers to understand their requirements, optimize their workflows, and improve platform usability

Qualification

PythonKubernetesInfrastructure as CodeGPU-based training clustersDistributed systemsContainerizationCollaborationCommunication skills

Required

Develop and operate our low-latency model inference platform, ensuring high availability, scaling, and efficient resource utilization for Odyssey's products

Engineer and scale our core data processing infrastructure (e.g., Flyte, Ray with k8s) to handle petabyte-scale datasets

Design, build, and maintain our large-scale, GPU-based training clusters for deep learning, focusing on high throughput and reliability

Automate infrastructure provisioning, configuration, monitoring, and alerting using Infrastructure as Code (IaC) principles

Drive performance tuning, cost optimization, and reliability improvements across the entire stack

Collaborate closely with researchers and product developers to understand their requirements, optimize their workflows, and improve platform usability

Strong programming skills (e.g., Python, Go, or similar) and a solid understanding of software engineering best practices

Deep, hands-on experience with containerization (e.g., Docker), container orchestration (Kubernetes) and Infrastructure as Code (Terraform)

Proven experience building and managing large-scale, distributed systems with GPU computational workloads (e.g., compute platforms, data pipelines, or high-availability services)

Experienced in designing infrastructure for ML workloads where performance, parallelism, and data movement are critical

A collaborative mindset and excellent communication skills, with a passion for building developer-friendly platforms

Motivated by building for the frontier: you want to shape the compute and infrastructure foundation of a lab redefining how people create and interact with media