Senior Staff Engineer, ML Ops (R4941)

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• Engineers move from idea to distributed training in hours instead of days.

• High GPU utilization through efficient scheduling with KAI on Kubernetes.

• Researchers use modern AI tooling without unnecessary platform friction.

• AI Workspaces become the standard development environment across autonomy programs.

• Training, simulation, evaluation, and deployment operate as a unified platform.

• The AI Factory Reference Architecture can be deployed consistently across cloud, on-premise, and air-gapped environments.

• Platform capabilities are reusable across multiple autonomy programs and customer deployments.

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• Experience building Kubernetes-native AI or MLOps platforms supporting distributed machine learning workloads.

• Deep understanding of modern AI training frameworks, including 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 concepts and large-scale AI workloads.

• Experience packaging and deploying cloud-native infrastructure using Terraform and Helm.

• Strong software engineering skills in Python and Golang and modern cloud-native technologies.

• Experience collaborating closely with ML researchers to translate research workflows into scalable platform capabilities.

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• Experience with Ray or other distributed AI orchestration frameworks.

• Experience with KAI, Slurm or other GPU scheduling technologies.

• Experience supporting reinforcement learning, simulation-driven training, robotics, or autonomy workloads.

• Experience deploying and optimizing AI models for edge hardware.

• Experience designing infrastructure for classified, sovereign, or air-gapped environments.

• Experience with observability technologies such as OpenTelemetry, Prometheus, and Grafana.

• Experience contributing to or maintaining open-source infrastructure projects.

• The AI Factory is the foundation for how Shield AI develops and deploys autonomy.

As a Senior Staff Engineer, you will help build the reference architecture used internally by our engineering teams and delivered to customers building their own AI capabilities. Your work will directly impact how quickly researchers can iterate, how efficiently compute resources are utilized, and how AI systems move from experimentation to operational deployment. This is an opportunity to work at the intersection of modern AI, distributed systems, Kubernetes, and defense, building a platform that enables the next generation of autonomy systems while leveraging the best of the open-source AI ecosystem.

#LI-DM2 #LF

Full-time regular employee offer package: Pay within range listed + Bonus + Benefits + Equity

Temporary employee offer package: Pay within range listed above + temporary benefits package (applicable after 60 days of employment)

Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.

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Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know.