2 months ago
Base Salary
$150k - $230k/yr
Responsibilities
- Lead the architecture and implementation of a knowledge graph and multimodal Graph API for human, service, and agentic workflows.
- Own storage, indexing, query, ingestion, and compute infrastructure across the data lifecycle.
- Define durable patterns for schema modeling, relationships, lineage, identity, and schema evolution.
- Build APIs, SDKs, tools, examples, diagnostics, and integrations for data collection, aggregation, and agentic access.
- Establish reference architectures, deployment patterns, benchmarks, operational guidance, and portable platform capabilities.
- Partner with autonomy, ML, test, infrastructure, product, and customer-facing teams to turn workflows into reusable platform capabilities.
- Evaluate emerging technologies and make build-versus-buy decisions while guiding implementation across teams.
- Raise standards for observability, performance, reliability, security, data integrity, disaster recovery, and lifecycle management.
Requirements
- Significant experience designing and operating distributed data solutions, storage systems, or data-intensive backend services.
- Strong software engineering skills and production experience with languages such as Go and Python.
- Deep understanding of data modeling, API design, schema evolution, identity, consistency, indexing, query planning, and data lifecycle concerns.
- Experience with multiple storage modalities, including relational or graph databases, object storage, analytical or columnar systems, and file storage.
- Experience designing reliable ingestion and access paths for high-volume or operationally important data.
- Strong understanding of Kubernetes, Linux, networking, security, storage, observability, and distributed-systems fundamentals.
- Experience deploying data infrastructure across cloud or customer-managed environments using Infrastructure as Code and platform engineering practices.
- Ability to evaluate technologies through prototypes, benchmarks, operational requirements, and lifecycle cost.
- Experience defining architecture and technical standards while remaining hands-on in implementation and debugging.
- Demonstrated collaboration with ML researchers, autonomy engineers, test teams, platform engineers, and product stakeholders.
- Preferred experience includes graph-backed retrieval, structured RAG, provenance-aware context, S3-compatible APIs, Apache Arrow, Parquet, OpenAPI, AsyncAPI, WebSockets, Terraform, Helm, GitOps, Ray, Kafka, NATS, Redpanda, ML data lifecycle systems, observability, distributed tracing, data security, governance, and auditability.
Benefits
- Full-time regular employee offer package includes benefits, bonus, equity, and pay within the listed range.
- Employment is contingent on a cleared background and possible reference check.
- The role is part of a full-time regular employee opportunity; temporary employee benefits are available after 60 days where applicable.
Tech Stack
Categories
BackendData Engineering
About Shield AI
Shield AI builds autonomous systems for military and national security customers, combining its Hivemind autonomy software with V-BAT and X-BAT unmanned aircraft and Aechelon simulation technologies. The privately held company sells hardware, software, and related services to U.S. and allied defense agencies. Founded in 2015 and headquartered in San Diego, it operates across the U.S., Europe, the Middle East, and Asia-Pacific, and its technology is used in operational deployments.