2 months ago
Base Salary
$280k - $420k/yr
Responsibilities
- Lead the design and implementation of the Kubernetes-native AI Factory Reference Architecture.
- Build infrastructure for distributed training, simulation, inference, reinforcement learning, evaluation, and deployment.
- Partner with ML researchers and autonomy teams to translate research workflows into scalable platform capabilities.
- Design self-service developer workflows from local experimentation through large-scale distributed execution.
- Design and optimize shared GPU infrastructure, including scheduling, storage, networking, observability, and reliability.
- Build dataset management, experiment tracking, artifact management, model versioning, evaluation, deployment, and monitoring capabilities.
- Develop repeatable Infrastructure as Code and platform lifecycle solutions for cloud, on-premise, sovereign, and air-gapped environments.
- Evaluate AI infrastructure technologies and establish scalable, maintainable architectural patterns.
- Collaborate with infrastructure engineers and product teams to evolve the platform for internal and customer use.
Requirements
- Experience building Kubernetes-native AI or MLOps platforms for distributed machine learning workloads.
- Deep understanding of PyTorch, Hugging Face Transformers, and distributed training techniques.
- Experience operating GPU-accelerated infrastructure and distributed training systems.
- Strong understanding of Kubernetes, Linux, networking, security, storage, and distributed systems.
- Experience with GPU scheduling and large-scale AI workloads.
- Experience packaging and deploying cloud-native infrastructure with Terraform and Helm.
- Strong software engineering skills in Python and Golang and modern cloud-native technologies.
- Experience collaborating closely with ML researchers to develop scalable platform capabilities.
- Preferred experience with Ray or other distributed AI orchestration frameworks.
- Preferred experience with KAI, Slurm, or other GPU scheduling technologies.
- Preferred experience supporting reinforcement learning, simulation-driven training, robotics, or autonomy workloads.
- Preferred experience deploying and optimizing AI models for edge hardware.
- Preferred experience designing infrastructure for classified, sovereign, or air-gapped environments.
- Preferred experience with OpenTelemetry, Prometheus, and Grafana.
- Preferred experience contributing to or maintaining open-source infrastructure projects.
Benefits
- Full-time regular employees are eligible for bonus, benefits, and equity in addition to pay within the listed range.
- Temporary employees receive a temporary benefits package applicable after 60 days of employment.
- Offers are contingent on a cleared background and possible reference check.
- Military fellows and part-time employees are not eligible for benefits.
Categories
About Shield AI
Shield AI builds autonomous systems for military and national security customers, combining its Hivemind autonomy software with V-BAT and X-BAT unmanned aircraft and Aechelon simulation technologies. The privately held company sells hardware, software, and related services to U.S. and allied defense agencies. Founded in 2015 and headquartered in San Diego, it operates across the U.S., Europe, the Middle East, and Asia-Pacific, and its technology is used in operational deployments.