2027 US AI Engineering – New College Graduate

Bitdeer Technologies Group•San Jose, CA

About The Position

Bitdeer AI is hiring 2027 new college graduates to develop its full-stack AI cloud platform. Engineers may work across cloud services, managed Kubernetes, AI model training and inference, AI agent platforms, and advanced AI systems. The work spans infrastructure and software for GPU computing, large-scale training and inference, cloud-native services, and enterprise AI applications. Successful candidates will be placed in an area based on interview assessment and business needs, with opportunities to rotate across roles for career development.

Requirements

Be a fresh graduate with a bachelor's, master's, or PhD degree, or have up to 2 years of related work experience.
Strong programming ability in one or more languages, including Go, Python, C++, Java, Rust, or related technologies.
Demonstrate enthusiasm for AI infrastructure, cloud-native technologies, and building large-scale, globally distributed systems.
Show achievements in academics, engineering projects, top-tier publications, open-source contributions, or programming/algorithm competitions.
Have a strong ownership mentality, engineering discipline, and commitment to meaningful impact early in a career.

Nice To Haves

Experience with Kubernetes, Docker, Helm, Terraform, OpenStack, Prometheus, Grafana, Argo, Istio, PostgreSQL, Redis, message queues, distributed storage, or other cloud-native technologies.
Experience developing or operating large-scale cloud platforms, Kubernetes platforms, AI training platforms, inference platforms, or Agent platforms.

Responsibilities

Develop large-scale, highly available AI cloud services, including GPU virtual machines, bare-metal and container services, networking, storage, billing, monitoring, security, and multi-region resource management.
Develop managed Kubernetes services, including GPU-native orchestration, intelligent scheduling, distributed workload management, cluster lifecycle management, observability, and resource isolation.
Build platforms supporting large-scale AI training and inference workloads.
Develop distributed training, model fine-tuning and deployment, inference acceleration, serverless inference, model APIs and evaluation, and production LLM and multimodal model-serving capabilities.
Develop enterprise AI Agent platforms, including Agent building, workflow orchestration, tool integration, retrieval-augmented generation, memory systems, sandbox execution, multi-Agent collaboration, evaluation, observability, security, and runtime infrastructure.
Develop GPU resource scheduling, distributed systems, high-performance networking, cloud-native infrastructure, MLOps and LLMOps platforms, AI application marketplaces, and automated operations systems for large-scale AI infrastructure.
Contribute to work in areas such as cloud platform engineering, AI operations and site reliability engineering, MaaS and model serving, AI training infrastructure, AI Agent and application engineering, AI and cloud security, or AI research.