Senior Staff Engineer, System Architect (R5843)

Job description

Job responsibilities

• 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,…
• 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.

Job requirements

• 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.