Shield AI builds autonomy systems for defense applications, including air, maritime, and space platforms operating in complex and contested environments.
We are building the AI Factory Reference Architecture, a Kubernetes-native platform for developing, training, evaluating, and deploying next-generation AI systems.
The AI Factory serves two purposes. Internally, it powers autonomy development across Hivemind and other AI programs. Externally, it becomes the reference architecture deployed into customer environments, spanning commercial cloud, on-premise infrastructure, sovereign deployments, and fully air-gapped systems.
We are looking for a Senior Staff Engineer to help define and build this platform. You will partner closely with ML researchers, platform engineers, and autonomy teams to deliver an exceptional developer experience for training and deploying modern AI models.
Success in this role requires balancing researcher productivity, platform simplicity, operational excellence, and long-term maintainability. You will work hands-on across the stack, helping shape both the platform architecture and its implementation while staying closely aligned with the rapidly evolving AI ecosystem.
What you'll do:
- AI Platform Development: Lead the design and implementation of the AI Factory Reference Architecture, delivering a Kubernetes-native platform for AI development, distributed training, simulation, evaluation, and deployment.
- AI Research Enablement: Partner directly with ML researchers to understand evolving training workflows and ensure the platform supports state-of-the-art AI frameworks, foundation model development, reinforcement learning, distributed training, and emerging research workflows.
- Developer Experience: Design self-service AI development workflows that enable engineers to move seamlessly from local experimentation to large-scale distributed execution using familiar open source tools and frameworks.
- Distributed AI Infrastructure: Build the infrastructure required to support distributed training, simulation, inference, and reinforcement learning workloads. Evaluate and integrate orchestration, scheduling, and resource management technologies to maximize scalability and developer productivity.
- Compute Platform: Design and optimize shared GPU infrastructure across cloud and on-premises environments. Improve resource utilization, scheduling efficiency, storage, networking, observability, and overall platform reliability.
- Data & Model Lifecycle: Build platform capabilities that enable dataset management, experiment tracking, artifact management, model versioning, evaluation, deployment, monitoring, and continuous model improvement.
- Platform Distribution: Develop repeatable deployment and lifecycle management solutions using Infrastructure as Code and modern platform engineering practices. Support commercial cloud, customer-managed infrastructure, sovereign environments, and fully air-gapped deployments.
- Technology Leadership: Evaluate emerging AI infrastructure technologies and establish architectural patterns that balance scalability, performance, maintainability, and developer experience.
- Cross-Functional Collaboration: Work closely with AI researchers, autonomy teams, infrastructure engineers, and product teams to ensure the platform evolves alongside customer needs and advances in AI.
Key Outcomes:
- 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.
Required qualifications:
- 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.
Preferred qualifications:
- 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.
Why Join Us
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.
Skills Required
- Experience building and operating ML infrastructure at scale (100+ GPU clusters, distributed systems)
- Experience defining compute strategy including on-premise vs cloud tradeoffs, capacity planning, and cost management
- Strong understanding of ML workloads including foundation models, RL/MARL, simulation-based training, and fine-tuning
- Experience building data platforms with dataset versioning, lineage, and cataloging
- Ability to debug and resolve system issues when needed
- Experience in defense or classified environments (e.g., air-gapped systems, SCIFs)
- Experience with simulation-heavy ML systems (robotics, autonomy, or similar domains)
- Experience deploying and optimizing models for edge hardware (distillation, quantization, pruning)
- Familiarity with HPC systems (schedulers, parallel storage, high-speed networking)
What the Team is Saying
Shield AI Compensation & Benefits Highlights
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Healthcare Strength — Healthcare coverage includes medical, dental, vision, and mental-health support, with company materials describing excellent coverage. Feedback suggests these offerings are comprehensive and consistently highlighted across official and third-party benefit lists.
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Equity Value & Accessibility — Equity is granted to all full-time hires, with RSU structures and tools like Carta Tax intended to improve understanding and tax timing. Feedback suggests this broad-based ownership approach is a notable component of total rewards.
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Parental & Family Support — Benefits include paid parental leave, fertility support, childcare benefits, family medical leave, and onsite resources such as a Mother's Room. Feedback suggests the family-oriented offerings are more expansive than basic coverage.
Shield AI Insights
What We Do
At Shield AI, you won't wait years to see your work reach the field. You'll build hardware and software that operates in the real world right now, in the hands of the people who depend on it. Hivemind, our AI pilot, has been flying since 2018. It has flown more than 30 platforms, including an F-16, and it now sits under a U.S. Air Force production contract for Collaborative Combat Aircraft. When you write code or shape a system here, you contribute to technology with a proven flight record and a clear production future. V-BAT flies intelligence, surveillance, and reconnaissance missions with an operational record that stretches from Ukraine to the Indo-Pacific. It delivers eyes where they matter most, in the most demanding conditions on earth. The teams behind it watch their work get tested where the stakes are real. X-BAT takes its first flight this year. It's an AI-piloted fighter that needs no runway, built to operate where traditional aircraft can't. Join now and you help shape a program at its earliest, most formative stage. That's the kind of ground-floor work that defines a career. Do the most impactful work of your life, on problems that matter. Autonomy at this level asks a lot of you. You'll take on problems in perception, planning, and control that few teams anywhere are equipped to solve. You'll work across disciplines, from aerospace and robotics to machine learning and systems engineering, alongside people who hold themselves to an exacting standard and expect the same from you. Our mission is clear: protect service members and civilians with intelligent systems. That purpose runs through every decision, every design review, and every deployment. It's why the work here carries a weight you can feel. Ready to join our mission? Explore our open roles and find where you fit.
Why Work With Us
Founded in 2015 by a former Navy SEAL, Shield AI builds AI pilots and uncrewed aircraft. Veterans aren't an afterthought here, they're at every level. It's why the work carries weight: AI pilots and uncrewed aircraft flying real missions, from Ukraine to the Indo-Pacific, protecting service members and civilians.
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Hybrid Workspace
Employees engage in a combination of remote and on-site work.
