Among computer and information research scientists, AI is already used for a large share of the tasks. Measured exposure for computer and information research scientists: 34% (high).

Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.

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About the job

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.

Job Description: Machine-learning engineers need reliable access to GPUs to train and evaluate models. Shield AI’s AI Factory provides shared compute and development tools for these workflows, both for our engineers and for customer deployments. We are looking for a Platform Engineer to make that compute platform easier to use, more reliable and more efficient. You will write software, improve how teams share GPUs, help developers troubleshoot workloads and use performance data to guide capacity growth. This is a hands-on software and platform engineering role.

What you'll do:

Build and maintain the software and configuration that let developers run GPU workloads on Kubernetes.

Design quotas, scheduling priorities, permissions and isolation so multiple teams can share compute predictably.

Automate deployment and upgrades of GPU services, including Run:ai, with infrastructure as code and tools such as Terraform and Helm.

Investigate why jobs wait, fail or run slowly, and fix recurring scheduling, configuration, storage and networking problems.

Build diagnostics that connect queue times, GPU utilization, memory requirements and training throughput to the developer’s experience.

Benchmark representative workloads and recommend when to add GPU capacity, how much to add and which hardware fits the workload.

Deliver repeatable installations and upgrades across cloud, on-premises and air-gapped environments.

Key Outcomes:

Developers can get the GPU resources they need through understandable access and resource policies.

Teams can diagnose slow or blocked training jobs and distinguish capacity shortages from configuration problems.

GPU expansion decisions reflect measured demand and workload performance.

Platform changes improve reliability without disrupting existing training workflows.

Required qualifications:

Experience building and supporting production Kubernetes platforms, including GPU or distributed computing workloads.

Strong software engineering skills in Python, Go or a comparable language.

Experience automating infrastructure deployment, configuration and upgrades.

Practical Linux, networking and storage troubleshooting skills.

Experience with shared resource scheduling, access controls or workload isolation.

Ability to work directly with machine-learning engineers, understand their workloads and turn recurring problems into maintained platform capabilities.

Preferred qualifications:

Experience with Run:ai or comparable GPU scheduling and resource-management systems.

Familiarity with distributed training and tools such as Ray or PyTorch.

Experience delivering on-premises or air-gapped platforms, or contributing to open-source infrastructure.

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.

#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.