Senior Staff Engineer, Shield AI

• 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

Desirable

• 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
• 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