About us
Gimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.
We combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.
We work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.
About the role
As an Infrastructure Platform Engineer, you will build the systems that turn heterogeneous accelerator hardware into reliable production infrastructure for Gimlet's AI cloud.
Gimlet's fleet spans hardware with different architectures, software stacks, operational characteristics, and failure modes. Your work will determine how new hardware is brought online, how clusters are provisioned and operated, and how production inference systems remain reliable as the fleet scales.
You will work across bare metal, Linux, Kubernetes, cluster scheduling, observability, and automation. You will build systems that abstract differences between accelerator architectures, make new hardware production-ready, and improve the reliability and operability of Gimlet’s infrastructure.
What success looks like
In your first 12–18 months, you will:
Deploy and operate production clusters across different accelerator architectures
Automate hardware provisioning, validation, upgrades, and fleet lifecycle management
Improve cluster scheduling, resource utilization, isolation, and capacity management
Build observable infrastructure that enables faster debugging, incident response, and recovery
Partner across distributed systems, runtime, compiler, networking, and hardware teams to bring new accelerators into production
You may be a good fit if you have
Experience in infrastructure, cluster engineering, platform engineering, SRE, or HPC
Strong Linux systems knowledge and production debugging experience
Experience operating Kubernetes, Slurm, Nomad, or similar orchestration systems
Experience automating infrastructure with Python, Go, Terraform, Ansible, or similar tools
Experience with GPU or accelerator infrastructure, including drivers, firmware, or CUDA/ROCm
The ability to build systems that are observable, recoverable, and reliable in production
A bachelor’s degree in a relevant field or equivalent practical experience
Strong candidates may also have
Experience building or operating AI inference, training, HPC, or neocloud infrastructure
Experience with bare-metal provisioning, PXE/iPXE, image pipelines, BIOS/firmware management, or rack bring-up
Experience with multi-tenant cluster isolation, quota systems, fair scheduling, or usage accounting
Experience debugging distributed workload performance across compute, memory, network, and storage bottlenecks
Experience building observability platforms using technologies such as Prometheus, OpenTelemetry, Grafana, or similar tooling
Familiarity with heterogeneous hardware environments across NVIDIA, AMD, Intel, ARM, or emerging accelerators
Why join now?
Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.
Solve hard problems.
Own meaningful work.
Build for production.
Help define what’s next.
Agency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid.