2027 US AI Engineering

Responsibilities

• Develop large-scale, highly available AI cloud services including GPU virtual machines, bare-metal services, containers, networking, storage, billing, monitoring, security, and multi-region resource management.
• Build managed Kubernetes services with GPU orchestration, intelligent scheduling, distributed workload management, cluster lifecycle management, observability, and resource isolation.
• Develop distributed training, fine-tuning, model deployment, inference acceleration, serverless inference, model APIs, evaluation, and production model-serving systems.
• Build enterprise AI agent platforms involving agent construction, workflow orchestration, tool integration, retrieval-augmented generation, memory, sandbox execution, multi-agent collaboration, evaluation, observability, security, and runtime infrastructure.
• Work on GPU scheduling, distributed systems, high-performance networking, cloud-native infrastructure, MLOps and LLMOps platforms, AI marketplaces, and automated operations systems.
• Join an assigned engineering area based on interview assessment and business needs, with opportunities to rotate across roles.

Requirements

• Fresh graduates with a bachelor's, master's, or PhD in any discipline, or candidates with up to two years of related work experience.
• Strong programming ability in one or more of Go, Python, C++, Java, Rust, or related technologies.
• Experience with Kubernetes, Docker, Helm, Terraform, OpenStack, Prometheus, Grafana, Argo, Istio, PostgreSQL, Redis, message queues, distributed storage, or other cloud-native technologies is highly preferred.
• Experience developing or operating large-scale cloud, Kubernetes, AI training, inference, or AI agent platforms is highly preferred.
• Enthusiasm for AI infrastructure, cloud-native technologies, and globally distributed systems.
• Demonstrated achievement through academics, engineering projects, publications, open-source contributions, or programming and algorithm competitions.
• Strong ownership, engineering discipline, and commitment to creating impact early in a career.

Benefits

• Inclusive and respectful workplace with diverse perspectives, open workspaces, and a startup-oriented environment.
• Opportunities to network with industry pioneers and contribute directly to new projects and systems.
• Personal accountability, autonomy, rapid growth, learning opportunities, training, and mentoring.
• Opportunities to rotate across roles as part of career development.