We’re seeking a Staff Software Engineer to lead technical direction and architectural decisions for a secure, AI-enabled data platform. This role blends hands-on software engineering with MLOps and data infrastructure expertise.
You’ll be responsible for designing, building, and supporting mission-critical services across the full data stack. The ideal candidate brings deep experience delivering secure, large-scale production systems and thrives in high-ownership, ambiguous environments. Familiarity with national security or regulated environments is a strong plus, as understanding complex stakeholder requirements will directly inform the product roadmap.
This is an AI-native engineering team. Engineers are expected to leverage LLMs and AI tooling as a core part of their daily workflow—from system design and coding to debugging and deployment.
Own architectural decisions and technical strategy for a secure, production-grade platform
Design and scale systems that handle sensitive, large-scale data
Contribute hands-on across backend services, infrastructure, and ML-enabled components
Shape engineering culture and best practices in a high-security, high-impact environment
Integrate AI tools and LLM-driven workflows into development and operational processes
Collaborate with cross-functional stakeholders to translate complex requirements into scalable solutions
12–20+ years of engineering experience, including time operating at Staff level or above
Proven track record owning architecture for large-scale, mission-critical production systems
Systems thinker with strong judgment around trade-offs and long-term scalability
Clear communicator who can engage both technical and non-technical stakeholders
Comfortable operating with ambiguity and full ownership
Experience handling and securing large-scale production data
Strong proficiency with AWS (e.g., S3, Lambda, RDS)
Experience with ML development in Python and/or JavaScript
Interest in solving complex data-sharing challenges with modern AI approaches
Familiarity with regulated or high-security environments is a plus
Daily use of AI coding tools (e.g., Claude Code, Cursor, Copilot)
Experience leveraging LLMs across architecture design, debugging, and development workflows
Strong understanding of AI limitations and when to verify outputs
Familiarity with LLM integration frameworks (e.g., MCP or similar)