Boundless is coordinating GPU compute at scale as it becomes a leader in AI. As a Senior Infrastructure Engineer (GPU Compute), you'll build and operate the compute fabric that powers our AI inference workloads — a large, heterogeneous, globally distributed GPU fleet spanning consumer cards (including RTX 5090) and datacenter hardware. Your job is to keep that fleet full, fast, cheap, and always on: orchestrating workloads across regions and providers, squeezing every bit of performance out of the hardware, and driving down cost per GPU-hour. This role rewards engineers who want to go deep on bare-metal and GPU optimization.You should be comfortable operating with a high degree of autonomy, navigating ambiguity, and defaulting to a strong bias for action.What You'll DoGPU Fleet Orchestration: Operate a heterogeneous, multi-region GPU fleet (consumer + datacenter, including RTX 5090) using tools like SkyPilot, Kubernetes/k3s, and cloud + on-prem providers. Build the patterns that let us schedule inference workloads across the entire fleet reliably.Compute Scheduling & Utilization: Maximize GPU utilization across inference workloads. Own workload placement across spot, on-prem, and cloud capacity, keeping the "always-on inference substrate" saturated and economical.Bare-Metal & GPU Optimization: Go deep on GPU performance — PCIe P2P, ReBAR, NUMA topology (e.g. EPYC SP5), CUDA/driver tuning, memory configuration, and network topology — to push throughput per node.Reliability, Access & Observability: Build secure fleet access (Tailscale, Teleport), robust observability and alerting, and zero-downtime rollouts across a distributed node fleet.Cost Optimization: Drive down $/GPU-hr through spot instance management, intelligent workload placement between on-prem and cloud, and resource scheduling — without sacrificing reliability.Requirements5+ years of infrastructure/DevOps experience operating large-scale production systemsDeep expertise in Kubernetes, Docker, and container orchestration at scaleStrong Linux systems administration skillsProficiency in infrastructure-as-code tools (Terraform, Ansible, Pulumi)Track record of managing mission-critical, high-throughput systemsStrong infrastructure-as-code background in heterogeneous environmentsProficiency in at least one common scripting or programming language (Python, Bash, TypeScript, Go, etc.)Comfort navigating ambiguity with a strong bias for actionNice to HaveExperience with GPU computing infrastructure (CUDA, bare-metal optimization, kernel tuning)Experience operating ML training or other large-scale distributed compute infrastructureExperience with GPU fleet orchestration (SkyPilot, Ray, Slurm)Familiarity with fleet access and networking tooling (Tailscale, Teleport)Knowledge of network optimization and topology designExperience with multi-region, globally distributed systemsProficiency in Rust or low-level systems programmingExperience with on-premises data center operationsAdditional RequirementsCandidates must include a public GitHub profile in their application.The GitHub profile should demonstrate a minimum of 1 year of activity/history.Applications that do not include a GitHub profile, or show insufficient activity, will not be considered.BenefitsAt Boundless, we take care of our people, because building the future of AI compute starts with an empowered team. Here's what you can expect when you join us:Competitive salary (proposed band b/t US$175k and $250k annually) + equity allocationHealth, dental, vision (for U.S. employees; region-adjusted globally)Flexible PTOProfessional development and conference travel budgetRemote-first with regular off-sites and a high-trust, high-velocity team environmentWe are a global team, and applicants from around the world are welcome to apply.
Boundless is coordinating GPU compute at scale as it becomes a leader in AI. As a Senior Infrastructure Engineer (GPU Compute), you'll build and operate the compute fabric that powers our AI inference workloads — a large, heterogeneous, globally distributed GPU fleet spanning consumer cards (including RTX 5090) and datacenter hardware. Your job is to keep that fleet full, fast, cheap, and always on: orchestrating workloads across regions and providers, squeezing every bit of performance out of the hardware, and driving down cost per GPU-hour. This role rewards engineers who want to go deep on bare-metal and GPU optimization.
You should be comfortable operating with a high degree of autonomy, navigating ambiguity, and defaulting to a strong bias for action.
What You'll Do
GPU Fleet Orchestration: Operate a heterogeneous, multi-region GPU fleet (consumer + datacenter, including RTX 5090) using tools like SkyPilot, Kubernetes/k3s, and cloud + on-prem providers. Build the patterns that let us schedule inference workloads across the entire fleet reliably.
Compute Scheduling & Utilization: Maximize GPU utilization across inference workloads. Own workload placement across spot, on-prem, and cloud capacity, keeping the "always-on inference substrate" saturated and economical.
Bare-Metal & GPU Optimization: Go deep on GPU performance — PCIe P2P, ReBAR, NUMA topology (e.g. EPYC SP5), CUDA/driver tuning, memory configuration, and network topology — to push throughput per node.
Reliability, Access & Observability: Build secure fleet access (Tailscale, Teleport), robust observability and alerting, and zero-downtime rollouts across a distributed node fleet.
Cost Optimization: Drive down $/GPU-hr through spot instance management, intelligent workload placement between on-prem and cloud, and resource scheduling — without sacrificing reliability.
Requirements
- 5+ years of infrastructure/DevOps experience operating large-scale production systems
- Deep expertise in Kubernetes, Docker, and container orchestration at scale
- Strong Linux systems administration skills
- Proficiency in infrastructure-as-code tools (Terraform, Ansible, Pulumi)
- Track record of managing mission-critical, high-throughput systems
- Strong infrastructure-as-code background in heterogeneous environments
- Proficiency in at least one common scripting or programming language (Python, Bash, TypeScript, Go, etc.)
- Comfort navigating ambiguity with a strong bias for action
Nice to Have
- Experience with GPU computing infrastructure (CUDA, bare-metal optimization, kernel tuning)
- Experience operating ML training or other large-scale distributed compute infrastructure
- Experience with GPU fleet orchestration (SkyPilot, Ray, Slurm)
- Familiarity with fleet access and networking tooling (Tailscale, Teleport)
- Knowledge of network optimization and topology design
- Experience with multi-region, globally distributed systems
- Proficiency in Rust or low-level systems programming
- Experience with on-premises data center operations
Additional Requirements
- Candidates must include a public GitHub profile in their application.
- The GitHub profile should demonstrate a minimum of 1 year of activity/history.
- Applications that do not include a GitHub profile, or show insufficient activity, will not be considered.
Benefits
At Boundless, we take care of our people, because building the future of AI compute starts with an empowered team. Here's what you can expect when you join us:
- Competitive salary (proposed band b/t US$175k and $250k annually) + equity allocation
- Health, dental, vision (for U.S. employees; region-adjusted globally)
- Flexible PTO
- Professional development and conference travel budget
- Remote-first with regular off-sites and a high-trust, high-velocity team environment
We are a global team, and applicants from around the world are welcome to apply.
