Staff Software Engineer, Lattice

• 8+ years of professional experience writing and maintaining production-level code, with 5+ years in designing, delivering, and operating AI/ML systems in production
• Deep production experience with LLM systems (prompting, RAG, agent orchestration, evaluation frameworks, fine-tuning)
• Experience building and operating agentic systems (multi-step workflows, multi-agent topologies) and managing their failure modes
• Strong command of AI evaluation methodology and statistical experimentation
• Strong system design judgment across scalability, latency, accuracy, reliability, and cost
• Production-grade Python (clean, maintainable, testable systems)
• Experience with LangGraph (or comparable agent orchestration frameworks) and LLM observability/evaluation tooling (e.g., LangSmith)
• Vector databases and retrieval system design (Pinecone or similar)
• Experience operating AI systems in AWS or comparable cloud environments, including CI/CD, monitoring, and deployment workflows
• Familiarity with TypeScript is a plus
• Actively engaged in AI research and industry trends

Desirable

• At Lattice, we build software that helps people and organizations thrive. Our AI Engineering team defines how intelligence works across our platform - how AI systems are measured, improved, and trusted in production
• This Staff-level role shapes the foundations that determine AI quality, reliability, and impact at scale
• You will architect and scale the infrastructure that powers AI quality, reliability, and reuse across Lattice
• Design and scale an end-to-end AI evaluation framework spanning offline evals, production tracing, and human feedback loops
• Define meaningful performance metrics (task completion, hallucination, response quality, engagement, business impact) and build the datasets and automated scoring systems that prevent regressions
• Identify and quantify the drivers of agent quality improvement and set methodological standards for evaluation across the organization
• Architect reusable agent infrastructure (multi-turn workflows, LLM DAGs, recommendation systems, standardized topologies) using LangGraph or comparable frameworks
• Build and scale RAG pipelines, vector retrieval systems, and production-grade AI infrastructure with strong reliability, observability, and performance
• Make principled build-vs-buy decisions across LLM providers, agent frameworks, and evaluation tooling, balancing capability, cost, latency, and risk
• Engineer AI systems as reusable internal platforms that multiply product engineering velocity at Lattice
• Own projects end-to-end: scope, design, execution, and delivery
• Set technical direction for agent quality and evaluation strategy across Lattice engineering teams
• Lead rigorous discussions on AI system design and evaluation methodology
• Raise the AI engineering bar through mentorship, code review, and clear technical communication across engineering and leadership