Mid-level Software Engineer (Generative AI Cloud Infrastructure) - Perm - US/UK/Europe

Mid-Level Software Engineer – AI Cloud & LLM Infrastructure
Full-Time · Remote or Hybrid · Founding Team Opportunity

About Us

We are building a Gen AI Acceleration Cloud an end-to-end platform for the full generative AI lifecycle. Our focus is to deliver blazing-fast LLM inference, scalable fine-tuning, and modern AI cloud infrastructure that GPUs, SmartNICs/DPUs, and ultra-fast networking fabrics.

Our platform powers mission-critical workloads with:
● On-demand & managed Kubernetes clusters
● Slurm-based training clusters
● High-performance inference services
● Distributed fine-tuning and eval pipelines
● Global data centers &heterogeneous GPU fleets
We are looking for a jr-mid Software Engineer to design, build, and scale the core systems behind our AI cloud.

What You’ll Work On

AI Cloud Infrastructure

• Develop and maintain reliable backend services running across cloud data centers.
• Assist in building automation for GPU management, VM provisioning, and high-throughput storage systems.
• Contribute to distributed systems and pipelines that support AI workloads.

LLM & GPU Virtualization Platform

• Help build the software layer for GPU clusters with modern accelerators (H100, GB200, GB300).
• Work on GPU virtualization and management (PCIe passthrough, MIG, SR-IOV) under guidance.
• Support scaling and optimization of storage and data systems for AI training datasets.

Observability, Reliability & Automation

• Contribute to monitoring and observability stacks (Prometheus, Grafana, OpenTelemetry).
• Help implement automated node lifecycle management for distributed training and inference.
• Assist in building testing frameworks for resiliency and fault tolerance.

Core Platform Engineering

• Contribute to internal and open-source platform components.
• Build developer tooling, SDKs, and documentation for platform services.
• Support research and implementation for decentralized AI workloads under senior guidance.

Requirements

• 2–5 years of production software engineering experience.
• Proficiency in at least one backend language (Golang preferred; Python or Rust also valued).
• Experience contributing to distributed systems or high-performance services.

Cloud & Systems Knowledge

• Familiarity with cloud platforms (AWS, GCP, or Azure) and distributed microservices.
• Understanding of concurrency, memory management, and high-performance I/O.
• Exposure to system design and reliability concepts.

Infrastructure / DevOps Skills (Plus)

• Experience with Kubernetes, Docker, or similar container orchestration.
• Familiarity with Terraform, Ansible, CI/CD pipelines, and monitoring tools.

Virtualization & Compute (Optional / Nice to Have)

• Exposure to GPU virtualization, CUDA, or distributed ML training stacks.
• Basic understanding of hypervisors or PCIe passthrough.

Networking (Optional / Nice to Have)

• Familiarity with VLAN/VXLAN, RDMA/Infiniband, or high-performance networking concepts.

Responsibilities

• Build and maintain backend and infrastructure components for AI workloads.
• Collaborate with senior engineers on GPU clusters, storage systems, and virtualization platforms.
• Assist in end-to-end service delivery from design to operation.
• Contribute to testing frameworks and automation for reliability.
• Work closely with cross-functional teams including ML engineers, product, and hardware teams.

Who You Are

• A technically curious engineer who enjoys complex systems work.
• Able to communicate ideas clearly and document work for others.
• Motivated by building infrastructure that supports cutting-edge AI.
• Collaborative, adaptable, and comfortable in a fast-moving startup environment.