Senior Staff Engineer Accelerated Compute (GPU Infrastructure)
Application Form
Senior Staff Engineer Accelerated Compute (GPU Infrastructure) Application Form
Role: Senior / Staff Engineer — Accelerated Compute (GPU Infrastructure)
Function: Engineering
Location: Bengaluru, India (Hybrid/Remote)
Type: Full-time
Industry: AI infrastructure, Cloud Computing
About Company
This role is with a rapidly growing AI infrastructure startup founded in 2025 in Bengaluru by a leadership team with deep product, cloud, and systems experience from global-scale tech companies. The company has built a GenAI-powered private cloud platform that automates and manages complex AI workloads across hybrid, on-prem, edge, and sovereign cloud environments — designed for enterprise sectors where performance, data security, and compliance are critical. Backed by leading global VCs and prominent operators (approx. $10M seed raised), the company is recognized for strong engineering rigor and product clarity. Its platform focuses on AI-native orchestration, deep observability, and cost/performance optimization to help large enterprises deploy and scale AI with confidence. This is an opportunity to join early and shape the future of AI-first cloud infrastructure.
Position Overview
You own the GPU infrastructure that powers a new kind of AI-focused cloud. You design and automate systems that make high-performance compute as simple as an API call. You join early, influence architecture, and shape how enterprise customers run massive AI workloads.
Role & Responsibilities
- Own end-to-end GPU enablement across the platform, from design to production support.
- Implement and manage NVIDIA vGPU across multiple hypervisors so several VMs share GPUs efficiently.
- Extend Kubernetes to become GPU-aware and build custom device plugins for MIG instances.
- Use NVIDIA MIG to partition GPUs into hardware-isolated slices exposed as rentable units.
- Create and maintain pre-configured VM images optimized for GPU workloads, including drivers and libraries such as MPI and NCCL.
- Develop automation that lets customers spin up multi-node, InfiniBand-connected GPU clusters with a single API call.
Must have Criteria
- Proven background in cloud, SRE, or infrastructure engineering with a compute focus.
- Hands-on expertise with NVIDIA GPU technologies such as vGPU, MIG, and CUDA.
- Strong knowledge of hypervisors including KVM, VMware, or XenServer.
- Experience with high-performance networking technologies like InfiniBand, RDMA, and IPoIB.
- Production-grade Kubernetes skills.
Nice to Have
- Experience writing custom Kubernetes schedulers or device plugins.
Apply Now
Share your details below to apply for this job.
Job Description
Role: Senior / Staff Engineer — Accelerated Compute (GPU Infrastructure)
Function: Engineering
Location: Bengaluru, India (Hybrid/Remote)
Type: Full-time
Industry: AI infrastructure, Cloud Computing
About Company
This role is with a rapidly growing AI infrastructure startup founded in 2025 in Bengaluru by a leadership team with deep product, cloud, and systems experience from global-scale tech companies. The company has built a GenAI-powered private cloud platform that automates and manages complex AI workloads across hybrid, on-prem, edge, and sovereign cloud environments — designed for enterprise sectors where performance, data security, and compliance are critical. Backed by leading global VCs and prominent operators (approx. $10M seed raised), the company is recognized for strong engineering rigor and product clarity. Its platform focuses on AI-native orchestration, deep observability, and cost/performance optimization to help large enterprises deploy and scale AI with confidence. This is an opportunity to join early and shape the future of AI-first cloud infrastructure.
Position Overview
You own the GPU infrastructure that powers a new kind of AI-focused cloud. You design and automate systems that make high-performance compute as simple as an API call. You join early, influence architecture, and shape how enterprise customers run massive AI workloads.
Role & Responsibilities
- Own end-to-end GPU enablement across the platform, from design to production support.
- Implement and manage NVIDIA vGPU across multiple hypervisors so several VMs share GPUs efficiently.
- Extend Kubernetes to become GPU-aware and build custom device plugins for MIG instances.
- Use NVIDIA MIG to partition GPUs into hardware-isolated slices exposed as rentable units.
- Create and maintain pre-configured VM images optimized for GPU workloads, including drivers and libraries such as MPI and NCCL.
- Develop automation that lets customers spin up multi-node, InfiniBand-connected GPU clusters with a single API call.
Must have Criteria
- Proven background in cloud, SRE, or infrastructure engineering with a compute focus.
- Hands-on expertise with NVIDIA GPU technologies such as vGPU, MIG, and CUDA.
- Strong knowledge of hypervisors including KVM, VMware, or XenServer.
- Experience with high-performance networking technologies like InfiniBand, RDMA, and IPoIB.
- Production-grade Kubernetes skills.
Nice to Have
- Experience writing custom Kubernetes schedulers or device plugins.
Apply Now
Share your details below to apply for this job.
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