Principal Software Engineer, AI Compute Infrastructure
- Compensation
- $262.7K–$355.4K / yr
- Location
- Seattle, Washington, United States
- Work setup
- Hybrid
- Seniority
- Principal
- Employment
- Not specified
- Experience
- 12+ years
- Category
- Software engineering
- Posted
Skills
Nice to have
- Familiarity with Kubernetes scheduling, operators, quotas, or resource management.
- Experience with NVIDIA technologies such as CUDA, NVLink, NVSwitch, NCCL, EFA, or DCGM.
- Knowledge of AWS EKS, Terraform, Argo CD, Helm, Prometheus, or Grafana.
- Familiarity with frameworks such as PyTorch, Ray, vLLM, SGLang, or TensorRT-LLM, or experience qualifying accelerators and tuning distributed workloads.
Full description
As a Principal Engineer on the AI Compute Infra team, you will design, build, and operate large-scale infrastructure for AI training, fine-tuning, evaluation, and inference. You will guide work across Kubernetes clusters, accelerator enablement, workload scheduling, high-performance networking, storage, and capacity management, partnering with AI researchers and engineers to improve reliability, performance, scalability, and developer productivity.
Responsibilities:
- Build and operate Kubernetes clusters while improving workload scheduling, topology-aware placement, capacity use, and recovery.
- Enable new CPU and GPU systems by integrating and validating drivers, networking, storage, monitoring, and health checks.
- Investigate performance and reliability issues across applications, cloud infrastructure, clusters, and hardware, then turn findings into lasting improvements.
- Partner with AI teams to understand their workloads and automate cluster provisioning, upgrades, monitoring, and maintenance around their needs.
Necessary Skills and Experience:
- 12+ years of experience building or operating cloud, compute, HPC, or distributed infrastructure in a production environment.
- Programming experience in Go, Python, or another systems language, with an interest in developing reliable infrastructure software.
- Practical knowledge of Kubernetes, containers, Linux, networking, and storage.
- Experience supporting GPU, accelerator, or distributed machine-learning workloads.
- An ability to troubleshoot complex systems and communicate clearly with engineers from different technical backgrounds.
“Preferred” Skills and Experience:
- Familiarity with Kubernetes scheduling, operators, quotas, or resource management.
- Experience with NVIDIA technologies such as CUDA, NVLink, NVSwitch, NCCL, EFA, or DCGM.
- Knowledge of AWS EKS, Terraform, Argo CD, Helm, Prometheus, or Grafana.
- Familiarity with frameworks such as PyTorch, Ray, vLLM, SGLang, or TensorRT-LLM, or experience qualifying accelerators and tuning distributed workloads.
In Return:
You will join a driven group committed to developing world-class AI compute infrastructure. We provide a cooperative setting where your ideas can come to life. Your efforts will directly impact the success of our AI projects, guaranteeing smooth operations and outstanding results. Join us and help build the future of AI compute infrastructure!
Additional Information
Please note that a relocation package (including visa sponsorship support) is available for this role, for candidates who require it.
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.