Job Description
The Senior Cloud Infrastructure Engineer at Gatik is responsible for designing, building, and maintaining large-scale, high-performance cloud and Kubernetes infrastructure to support autonomous vehicle AI systems, including data pipelines, model management, and distributed training environments.
Key Responsibilities
- Architect and manage large-scale compute and data infrastructure supporting autonomous driving systems.
- Build and optimize Kubernetes clusters for GPU and TPU workloads, including GPU scheduling and resource utilization.
- Implement Infrastructure as Code using tools like Terraform and Helm.
- Deploy and monitor autonomous AI agents for cluster health and hardware failure triage.
- Develop and maintain large-scale data pipelines using Apache Airflow, Kafka, and Spark.
- Automate deployment workflows with GitOps tools such as ArgoCD and GitLab CI/CD.
- Maintain infrastructure and model performance observability using Prometheus, Grafana, and OpenTelemetry.
- Design and manage ML model lifecycle tracking and automated workflows for training and deployment.
- Support deployment of models into simulation and production environments using Triton, Ray Serve, and ONNX Runtime.
- Enable scalable multi-node training of models with frameworks like PyTorch Distributed, Ray, and Horovod.
- Optimize low-level networking for large-scale training, including NCCL tuning and InfiniBand configuration.
- Collaborate with researchers to fine-tune multi-node GPU clusters for high-performance model training.
Requirements
- Five or more years of experience in Cloud Infrastructure, DevOps, or MLOps supporting high-scale compute environments.
- Deep expertise in Kubernetes, Helm, and container orchestration.
- Strong background in orchestration tooling such as Apache Airflow, Argo Workflows, MLFlow, and Terraform.
- Practical experience supporting distributed systems frameworks like Ray and PyTorch Distributed.
- Proficiency in Python and Bash scripting, with a solid understanding of IAM RBAC.
- Experience supporting training systems that enable scaling models across multi-node setups using PyTorch Distributed, Ray Train, and Horovod.
- Experience in optimizing low-level communication protocols such as NCCL tuning, InfiniBand, or RoCE v2 to minimize latency for large-scale training.
- Partnering with researchers to fine-tune performance across multi-node GPU clusters for FSDP and DeepSpeed workloads.
- Onsite role requiring work five days a week at the Mountain View, CA office.
Benefits & Perks
Salary range of $180,000 to $240,000
Onsite work schedule, 5 days a week at Mountain View, CA
Work environment perks include a culture emphasizing collaboration, respect, agility, diversity, inclusion, and opportunities for growth
Ready to Apply?
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