Staff Engineer — Accelerated Compute

12 hours ago


Bengaluru, Karnataka, India Gloroots Full time ₹ 12,00,000 - ₹ 36,00,000 per year

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 recognised for strong engineering rigour 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 computing 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.

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