Firmus jobs

Cost Engineer, Data Center IT Fit-Out Singapore Firmus Technologies Firmus Technologies is a global leader pioneering the solution to AI's energy challenge, founded in Australia in 2019 by a visionary team of entrepreneurs. Our mission is to create the most energy-efficient AI infrastructure, combining cutting edge technology with a steadfast commitment to sustainability. Through ground-breaking research and development, we invented a verticalized AI Factory - a new class of digital infrastructure that replaces traditional data centres. Built on new approaches to liquid cooling, energy management, water use and modular construction methodology, the Firmus AI Factory delivers low-cost AI tokens across Asia-Pacific. Firmus AI Cloud We provide customers access to energy savings via our large-scale GPU cloud, Firmus AI Cloud. Rated Silver in The GPU Cloud ClusterMAX™ Rating System, our cloud empowers developers, enterprise, education and government users to train AI models with unmatched efficiency and cost savings. With an ever-growing list of services and applications, we are committed to building a cloud experience for our customers that is market-leading, proprietary and built to scale. Why you'll love working here - A fast-paced and dynamic environment working with next-gen technology. You'll be operating at the intersection of sustainability and artificial intelligence - helping to transform an industry. - Working with and access to colleagues who are true innovators and leaders in their field. - As an emerging company, we work as a close-knit team. Work with the founders, grow a strong network, and witness the impact you make first-hand as we

Senior HPC Infrastructure Engineer Sydney, Australia Role Summary Firmus is seeking a highly skilled and driven Kubernetes HPC Engineer to join our Software Defined Infrastructure team. In this role, you will build high-performance, fault-tolerant, and reliable infrastructure to support bare-metal provisioning, performance benchmarking, and platform validation. You will be instrumental in ensuring the stability, performance, and continuous improvement of our complex and mission-critical bare-metal HPC GPU clusters. Key Responsibilities - Design and implement bare-metal provisioning workflows using Ironic and Kubernetes CRDs. - Deploy and manage GPU-enabled AI compute nodes with RDMA, InfiniBand, and RoCE networking. - Optimise Kubernetes and Slurm platforms for multi-node AI training performance, including NCCL, UCX, GPUDirect, and fabric tuning. - Implement Kubernetes primitives for GPU scheduling, isolation, and resource management models. - Design, deploy, and fine-tune Slurm GPU clusters with topology-aware configurations. - Develop and execute performance benchmarking workloads, including MLPerf, NCCL tests, microbenchmarks, and throughput/latency validation. - Establish observability across GPU, InfiniBand fabric, storage, and provisioning components. - Document architecture designs, operational procedures, and performance results. - Collaborate with L2 SRE engineers, site operations, and networking teams to ensure platform reliability, reproducibility, and performance. - Support hardware bring-up activities, including BIOS tuning, GPU topology verification, NUMA alignment, and PCIe/NVLink checks. - Contribute to continuous improvement in cluster validation, CI/CD automation, and provisioning and testing frameworks. - Contribute to the development of custom Kubernetes operators and intelligent orchestration frameworks that optimise AI workload performance for large-scale GPU cluster commissioning. Skills & Experience - Bachelor's or Master's

Senior AI Infrastructure Engineer (Virtualisation) Australia or Singapore Role Summary Firmus is seeking a highly skilled and driven Senior Engineer to play a key role in designing, building, and operating software-defined infrastructure, including high-performance AI storage platforms. You will help evolve our Software Defined Infrastructure by building reliable, scalable solutions that power some of the world's largest and most innovative AI workloads. You will be instrumental in ensuring the stability, performance, and continuous improvement of our mission-critical control plane and storage infrastructure. Key Responsibilities - Design and implement a highly scalable, multi-tenant control plane that supports Firmus' growing AI and infrastructure needs. - Contribute to the development of exabyte-scale, S3-compatible object storage, distributed file systems, and high-performance filesystems. - Work with bare-metal provisioning tools such as Base Command Manager, Warewulf, Ironic, MaaS, and similar platforms. - Apply a deep understanding of operating systems, computer networks, software-defined storage, and high-performance applications. - Work with technologies including RDMA, GPU Direct Storage, RoCE, InfiniBand, DPDK, Ceph, Weka, DAOS, and others. - Collaborate with operations teams to monitor, analyse, and optimise internal clusters and storage platforms. - Document architecture designs, operational procedures, and performance results. - Collaborate with L2 SRE engineers, site operations, and networking teams to ensure platform reliability, reproducibility, and performance. - Contribute to continuous improvement in cluster validation, CI/CD automation, and provisioning and testing frameworks. - Apply knowledge of Kubernetes and composable storage clusters. - Contribute to the development of custom Kubernetes operators and intelligent orchestration frameworks to optimise AI workload

AI Engineer, AI & Applications Singapore or Australia Role Summary The AI Engineer will establish Firmus AI Factory as the foundation for efficient, production-grade distributed training by delivering pre-built training recipes (TorchTitan, Megatron etc.), evaluation benchmarks, and model guidance. You'll work with customers and internal teams to optimize training efficiency, define baselines, and document best practices. Your templates and benchmarks are the anchor point for our hyperscale customers' training workflows and our model arena differentiator. Key Responsibilities - Build production-ready training recipes using TorchTitan and Megatron-LM: model configs, parallelism strategies (FSDP, tensor/pipeline parallelism), checkpointing patterns. - Document parameter tuning for different scales (e.g., "to train Llama 7B on 8xH100s, use this config and expect X throughput"). - Create and validate multi-node NCCL communication patterns on AI Factory K8s/Slurm clusters. - Design and build benchmarking suites: accuracy, latency, throughput (tokens/sec), cost per token, energy efficiency, MFU. - Implement offline evaluation harnesses for standardized model comparison and leaderboard tracking. - Conduct fine-tuning experiments (LoRA, QLoRA) where they improve product outcomes (e.g., ops domain data), document gains. - Create training efficiency playbooks and publish benchmark results so customers can optimize workloads. - Partner with job scheduling and orchestration engineers on template integration and other AI engineers and software engineers on model optimization trade-offs for inferencing and AI applications. Skills & Experience - 5-7 years of experience in distributed machine learning (PyTorch/JAX, FSDP, DeepSpeed, multi-node training at 10+ GPUs). - Expert-level understanding of GPU optimization: utilization, memory patterns, communication bottlenecks (NCCL collectives). - Hands-on