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Senior Staff Engineer, ML Ops (R4941)

Shield AI · On-site

This listing is no longer verified as available.

MeritLog keeps this source-backed description for reference. Availability is not verified, and there is no application link here.

Last seen by MeritLog September 12, 2026Source: LeverSource version: lever-postings-v1

Source: the employer's Lever job board. Open the original listing for current details. Availability is not verified for this retained page.

Job details

Work model
On-site
Salary
Not listed by source
Location
San Mateo, California

Job description

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:   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.

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