Senior GPU Systems & Fabric Engineer

Bitdeer Group; San Jose, Austin (CA, TX); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Bitdeer is seeking a Senior GPU Systems & Fabric Engineer to bridge physical GPU/network infrastructure with Kubernetes, building a low-latency, high-bandwidth fabric for AI workloads.

Candidate will optimize drivers, CUDA/NCCL libraries, and MIG/vGPU multi-tenant strategies, while scaling bare-metal systems in a production/HPC environment. Collaboration across teams is essential.

Qualifications

• Bachelor's or Master's degree in CS, EE, or related field.
• 5+ years of systems engineering experience with Linux kernel internals, C, or Go.
• Hands-on with GPU architectures (NVIDIA H100/A100), CUDA runtimes, and RDMA/InfiniBand networking.
• Deep understanding of containerized environments and Kubernetes device plugin architecture.
• Experience operating, debugging, and scaling bare-metal systems in large-scale production or HPC environments.
• Familiarity with infrastructure automation (Terraform, Ansible, CI/CD pipelines).
• Strong problem-solving skills and ability to navigate hardware-software challenges.
• Excellent communication and cross-team collaboration.

Responsibilities

• Architect and maintain integrations for NVIDIA/AMD GPU device plugins and Kubernetes Operators.
• Configure and optimize high-performance host networking stacks (RDMA, SR-IOV, RoCEv2, InfiniBand).
• Build and manage automated hardware remediation pipelines using DCGM telemetry.
• Implement GPU slicing technologies (MIG, vGPU) for multi-tenant inference workloads.
• Profile and tune kernel parameters, device drivers, and libraries (CUDA, NCCL).
• Collaborate with Scheduling and Storage teams for topology-aware placement and data movement.
• Define and enforce bare-metal provisioning and OS hardening standards.
• Lead investigations into performance issues spanning hardware, fabric, and software.
• Mentor team members and document the AI hardware stack.

Skills

Linux kernel internals

C or Go

GPU architectures (NVIDIA H100/A100)

CUDA runtimes

Kubernetes device plugin architecture

RDMA / InfiniBand networking

Bare-metal HPC experience

Infrastructure automation (Terraform,/

CI/CD pipelines

Problem solving & debugging

Communication & collaboration

Education

Bachelor's or Master's degree in Computer Science or Electrical Engineering

Tools

Terraform

Ansible

CI/CD tooling

Job description

Bitdeer is a world-leading technology company for AI and Bitcoin mining infrastructure. Bitdeer is committed to providing comprehensive Bitcoin mining solutions for its customers and building AI computational infrastructure to support the AI revolution. Bitdeer handles complex processes involved in computing such as equipment procurement, transport logistics, data center design and construction, equipment management, and daily operations. Bitdeer also offers advanced cloud capabilities to customers with high demand for artificial intelligence. Headquartered in Singapore, Bitdeer has deployed data centers across multiple countries, including the United States, Norway, Bhutan, and Ethiopia. To learn more, visit https://ir.bitdeer.com/Position Overview

We are seeking a Senior GPU Systems & Fabric Engineer to serve as the critical bridge between our physical GPU/network infrastructure and the Kubernetes abstraction layer. You will be responsible for creating the high-performance 'hardware foundation' that makes AI-native cloud computing possible. This role requires deep expertise in Linux kernel internals, GPU architectures, and high-speed interconnects, as you will be tasked with transforming raw, bare-metal compute resources into scalable, resilient, and multi-tenant cloud primitives. You will drive the design of our fabric layer, ensuring that our AI workloads have the low-latency, high-bandwidth environment they require to perform at industry-leading speeds.

Key Responsibilities

• Architect and maintain integrations for NVIDIA/AMD GPU device plugins and Kubernetes Operators to expose hardware capabilities to the control plane.
• Configure and optimize high-performance host networking stacks, including RDMA, SR-IOV, RoCEv2, and InfiniBand, ensuring line-rate throughput for distributed AI training.
• Build and manage automated hardware remediation pipelines using DCGM telemetry to proactively identify, isolate, and reset degraded GPU/NIC components before they impact production jobs.
• Implement and manage sophisticated GPU slicing technologies (MIG, vGPU) to enable efficient multi-tenant inference workloads and maximize cluster utilization.
• Profile and tune kernel-level parameters, device drivers, and runtime libraries (CUDA, NCCL) to resolve bottlenecks and optimize containerized AI workloads.
• Collaborate with the Scheduling and Storage engineering teams to ensure topology-aware placement and efficient data movement across the fabric.
• Define and enforce operational standards for bare-metal provisioning, BIOS/firmware updates, and OS hardening within the containerized environment.
• Lead technical investigations into complex performance issues spanning hardware, fabric, and software, providing actionable architectural insights.
• Mentor team members and drive documentation standards for our evolving AI hardware stack.

Qualifications

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field.
• 5+ years of systems engineering experience, with strong proficiency in Linux kernel internals, C, or Go.
• Hands-on experience with GPU architectures (NVIDIA H100/A100), CUDA runtimes, and distributed networking (RDMA, InfiniBand).
• Deep understanding of containerized environments and Kubernetes device plugin architecture.
• Proven track record of operating, debugging, and scaling bare-metal systems in large-scale production or HPC environments.
• Familiarity with infrastructure automation (e.g., Terraform, Ansible, CI/CD pipelines) for managing hardware lifecycles.
• Strong problem-solving skills, with the ability to navigate ambiguous performance challenges at the intersection of hardware and software.
• Excellent communication skills, with a collaborative approach to working across infrastructure, scheduling, and reliability teams.

Experience working in high-velocity, high-growth engineering environments is strongly preferred

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Bitdeer is committed to providing equal employment opportunities in accordance with country, state, and local laws. Bitdeer does not discriminate against employees or applicants based on conditions such as race, color, gender identity and/or expression, sexual orientation, marital and/or parental status, religion, political opinion, nationality, ethnic background or social origin, social status, disability, age, indigenous status, and union.