Shield AI is seeking an experienced System Architect to own the architecture of Hivemind Forge – an AI development ecosystem and the segment of Hivemind that enables developers to build, train, tune, evaluate, optimize, and deploy learning-based autonomy solutions. You will join the Systems Engineering, Integration, & Test (SEIT) team in the Hivemind Enterprise organization.
This ecosystem combines developer tools, APIs, data infrastructure, simulation, AI/ML workflows, and GenAI-enabled automation to accelerate the delivery of autonomous capabilities using Vision-Language Models (VLMs), Vision-Language-Action (VLA) models, world models, foundation models, and other applied AI technologies.
As the responsible architect, you will define the comprehensive architecture for this product area, maintain architectural integrity across development teams, and ensure the ecosystem can operate across enterprise cloud, high-performance computing, and secure or disconnected deployment environments. You will work at the intersection of software architecture, AI/ML, data architecture, systems engineering, developer experience, and Generative AI-enabled engineering to create an ecosystem that dramatically reduces the time required to transform mission needs and data into deployable, intelligent autonomous capabilities.
What you'll do:
- Own and evolve the architecture of the Hivemind Forge AI development ecosystem.
- Define and maintain architecture products in an integrated model-based systems engineering (MBSE) environment, connecting architectural intent to engineering execution.
- Establish architectural patterns, interfaces, APIs, SDK concepts, and technical standards across software, AI/ML, data, simulation, and deployment capabilities.
- Define the data and metadata architecture underpinning the AI development lifecycle, including schemas, relationships, lineage, provenance, versioning, and lifecycle management.
- Ensure traceable pedigree across datasets, training configurations, model artifacts, evaluations, software versions, and deployed capabilities to support reproducibility and auditability.
- Architect GenAI-enabled and agentic development workflows that accelerate data curation, autonomy development, experimentation, evaluation, troubleshooting, and deployment while preserving human oversight, security, verification, and traceability.
- Define architectural patterns for integrating AI coding assistants, agents, foundation models, and natural-language interfaces with Hivemind development tools, APIs, SDKs, data, simulation, and engineering workflows.
- Define architecture for scalable AI/ML workloads across cloud, high-performance compute (HPC), and on-premises infrastructure, including orchestration, workload scheduling, containerization, storage, and data movement.
- Ensure the ecosystem can be deployed and operated in classified, air-gapped, disconnected, and other constrained enterprise environments while preserving security, configuration control, and reproducibility.
- Ensure AI-assisted and agentic development workflows preserve appropriate provenance, traceability, human oversight, verification, security, and reproducibility, particularly when contributing to deployed autonomous capabilities.
- Guide architecture for workflows spanning data ingestion and curation, synthetic data generation, model training and tuning, evaluation, optimization, validation, and deployment.
- Review and approve detailed software and data designs, resolve cross-team architectural issues, and maintain architectural integrity through implementation and integration.
- Partner with software, AI/ML, data, systems, product, and technical leadership teams to reduce the time from mission need to validated, deployable autonomy.
Required qualifications:
- 10+ years of experience in software or software-intensive systems development, design, and/or architecture.
- Demonstrated experience architecting complex software platforms, developer ecosystems, SDKs, APIs, AI/ML platforms, or distributed systems.
- Experience developing AI/ML solutions using synthetic and real-world data.
- Experience in data modeling and data architecture, including metadata, lineage, provenance, versioning, and lifecycle management.
- Understanding of how data, training configurations, model artifacts, evaluations, software, and deployments must be connected to provide end-to-end traceability, reproducibility, and auditability.
- Experience applying Generative AI, AI assistants, or agents to software, AI/ML, or engineering development workflows.
- Practical understanding of technologies such as Kubernetes, Slurm or comparable workload schedulers, containerization, infrastructure as code, and object/data storage platforms such as S3-compatible systems.
- Strong technical leadership and communication skills, with the ability to guide detailed design and maintain alignment across multiple engineering teams.
Preferred qualifications:
- Experience designing AI-native or agentic workflows, including tool use, orchestration, retrieval, structured outputs, evaluation, and human-in-the-loop controls.
- Experience with MLOps, distributed training, simulation, synthetic data generation, experiment tracking, or model registries.
- Direct experience developing, training, tuning, evaluating, applying, or deploying VLMs, VLAs, world models, foundation models, or related modern AI models.
- Experience designing, deploying, or operating software and AI/ML platforms in classified, air-gapped, disconnected, or restricted-network environments.
- Experience with hybrid-cloud, multi-cloud, on-premises, or edge deployment architectures.
- Experience architecting autonomy, robotics, aerospace, unmanned systems, or other Physical AI applications.
- Experience applying MBSE methods and tools, including SysML and Cameo/MagicDraw.
- Experience delivering defense, aerospace, safety-relevant, or other high-assurance systems requiring rigorous configuration management, verification, and traceability.
Impact;
Skills Required
- 10+ years of experience in software or software-intensive systems development, design, and/or architecture.
- Experience architecting complex software platforms, developer ecosystems, SDKs, APIs, AI/ML platforms, or distributed systems.
- Experience developing AI/ML solutions using synthetic and real-world data.
- Experience in data modeling and data architecture, including metadata, lineage, provenance, versioning, and lifecycle management.
- Understanding of end-to-end traceability, reproducibility, and auditability across data, training configurations, model artifacts, evaluations, software, and deployments.
- Experience applying Generative AI, AI assistants, or agents to software, AI/ML, or engineering development workflows.
- Practical understanding of Kubernetes, Slurm or comparable workload schedulers, containerization, infrastructure as code, and S3-compatible object/data storage platforms.
- Strong technical leadership and communication skills, including the ability to guide detailed design and align multiple engineering teams.
- Experience designing AI-native or agentic workflows involving tool use, orchestration, retrieval, structured outputs, evaluation, and human-in-the-loop controls.
- Experience with MLOps, distributed training, simulation, synthetic data generation, experiment tracking, or model registries.
- Experience developing, training, tuning, evaluating, applying, or deploying VLMs, VLAs, world models, foundation models, or related modern AI models.
- Experience operating software and AI/ML platforms in classified, air-gapped, disconnected, or restricted-network environments.
- Experience with hybrid-cloud, multi-cloud, on-premises, or edge deployment architectures.
- Experience architecting autonomy, robotics, aerospace, unmanned systems, or other Physical AI applications.
- Experience applying MBSE methods and tools, including SysML and Cameo/MagicDraw.
- Experience delivering defense, aerospace, safety-relevant, or other high-assurance systems requiring configuration management, verification, and traceability.
What the Team is Saying
Shield AI Compensation & Benefits Highlights
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Healthcare Strength — Healthcare coverage is described as excellent, with dental/vision and mental‑health support, and ancillary protections like life and disability appearing in benefit summaries. The breadth and perceived affordability of coverage are highlighted as a standout component of the package.
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Parental & Family Support — Paid parental leave is featured alongside enhanced maternity benefits, fertility and childcare support, and onsite resources such as a Mother’s Room. These elements are positioned as competitive and above the minimal baseline for the company’s stage.
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Equity Value & Accessibility — Equity is granted to all full‑time hires, with RSUs, double‑trigger tax timing, and tools to model scenarios (e.g., through Carta Tax). Communications also reference a transition from options to RSUs, reinforcing access and maturity of ownership programs.
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.
