Staff Software Engineer, AI

Lattice•Salt Lake City, UT

About The Position

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. What You Will Do You will architect and scale the infrastructure that powers AI quality, reliability, and reuse across Lattice. AI Evaluation & Quality 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. Agent Architecture & Production Systems 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. Technical Leadership 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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.