AI Engineering – New College Graduate 2027

Bitdeer Technologies Group•San Jose, CA

4d•$70,000 - $130,000

About The Position

Bitdeer AI, a full-stack AI cloud provider (AI Data Center, IaaS, PaaS, MaaS) under Bitdeer Technologies Group and a preferred NVIDIA Cloud Partner, is engineered to power the AI of tomorrow. With the group’s 3 Gigawatts of power capacity deployed and under development globally, Bitdeer holds a decisive advantage in the AI era: the massive energy, physical infrastructure, and compute foundation required to run intelligence at global scale. Operating across strategic international hubs, including the United States, Singapore, and Europe, we build on one core promise: One Cloud, Infinite Possibilities. Our Two Core Missions: Transforming Hyperscale Power into Useful AI: We take the extreme complexity of gigawatt-scale energy grids, liquid-cooled data center topologies, and low-latency networking fabrics, and turn that raw physical power into accessible, high-performance AI Cloud that directly drives real-world intelligence. Building an Integrated Full-Stack Cloud Platform: We engineer the complete software layer, spanning bare-metal provisioning, managed Kubernetes, distributed training pipelines, high-throughput AI Inference engines, and Model-as-a-Service (MaaS) with serverless APIs, empowering enterprises to seamlessly train, fine-tune, deploy, and serve next-generation AI intelligence. We are seeking 2027 New College Graduates: Why Start Your Career at Bitdeer AI? We don’t just look at the infrastructure layer, we build across the entire value chain for our full-stack cloud. At Bitdeer AI, you will architect the actual backbone of global artificial intelligence. From foundation-level hardware orchestration and low-latency Inference systems to upper-layer MaaS platforms and AI Agents environments, you will solve high-scale systems challenges that few companies on Earth have the power capacity to attempt. Your work will directly power the global AI ecosystem, from ambitious startups and elite research institutions to global enterprises. Artificial intelligence is reshaping human potential, but the future of AI relies on real-world compute, reliable infrastructure and engineering, and massive power. Don’t just witness the AI revolution. Power it. Join Bitdeer AI, and build the intelligence for infinite possibilities.

Requirements

Fresh graduates (Bachelors, Masters, PhD) from all disciplines and/or candidates with 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.
Proven achievements in academics, engineering projects, top-tier publications, open-source contributions, or programming/algorithm competitions.
A strong ownership mentality, engineering discipline, and a commitment to creating a meaningful impact early in your career as we build the foundation for next-generation AI.

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 is highly preferred.
Experience in developing or operating large-scale cloud platforms, Kubernetes platforms, AI training platforms, inference platforms, or Agent platforms is highly preferred.
Deep enthusiasm for cutting-edge AI infrastructure, cloud-native technologies, and building large-scale, globally distributed systems.

Responsibilities

Development of large-scale, highly available AI cloud services, including GPU virtual machines, bare-metal services, container services, cloud networking, storage, billing, monitoring, security, and multi-region resource management.
Development of managed Kubernetes services with GPU-native orchestration, intelligent scheduling, distributed workload management, cluster lifecycle management, observability, resource isolation, and support for large-scale AI training and inference workloads.
Development of distributed training, model fine-tuning, model deployment, inference acceleration, serverless inference, model APIs, model evaluation, and production-grade LLM and multimodal model-serving capabilities.
Development of 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 large-scale Agent runtime infrastructure.
Development of GPU resource scheduling, distributed systems, high-performance networking, cloud-native infrastructure, MLOps and LLMOps platforms, AI application marketplaces, and automated operation systems for large-scale AI infrastructure.