Staff Software Engineer

### Who you are
- 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
- Experience with RLHF, LoRA, or other model adaptation techniques
- Background in traditional ML and judgment in selecting ML vs. LLM approaches
- Experience with MLOps tooling (MLflow, DataDog)
- Published research, talks, or open-source contributions in AI/ML
- Experience in HR tech or other trust-sensitive domains

### What the job involves
- 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