Staff Software Engineer, AI

About the Role

As a key member of the AI Engineering team, the Staff Software Engineer will be responsible for architecting and scaling the infrastructure that powers AI quality, reliability, and reuse across Lattice. This involves staying up-to-date with the latest advancements in AI research and industry trends and applying that knowledge to drive technical direction across engineering teams.

The team at Lattice is committed to building software that helps people and organizations thrive. This role is an opportunity to join a talented group of professionals who are passionate about AI and its potential to drive positive change.

What You Will Do

• Design and scale an end-to-end AI evaluation framework spanning offline evaluations, production tracing, and human feedback loops.
• Define meaningful performance metrics 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 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.

What We Are Looking For

• 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 and evaluation frameworks.
• Experience building and operating agentic systems 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 skills (clean, maintainable, testable systems).
• Experience with LangGraph (or comparable agent orchestration frameworks) and LLM observability/evaluation tooling.
• Vector databases and retrieval system design experience (Pinecone or similar).
• Experience operating AI systems in AWS or comparable cloud environments.
• Familiarity with TypeScript is a plus.
• Actively engaged in AI research and industry trends.

Nice to Have

• 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.

Benefits and Perks

• Competitive compensation package.
• Opportunities for professional growth and development in a rapidly expanding field.
• Collaborative and dynamic work environment with a team of talented professionals.
• Flexible remote work arrangements to ensure work-life balance.
• Access to cutting-edge technologies and tools.
• Recognition and reward for outstanding performance and contributions.
• Comprehensive health benefits and wellness programs.
• Generous PTO and holiday package.
• Retirement savings plan with company match.
• Ongoing training and education opportunities to stay current with industry trends.

• Ensure you have hands-on experience with AI evaluation frameworks and large language models, as these are critical for success in this role.
• Develop a strong understanding of system design principles, including scalability, reliability, and performance, to drive technical direction across engineering teams.
• Build a portfolio that showcases your experience with production-grade Python, LangGraph, and other relevant tools and technologies.
• Prepare to discuss your experience with AI system design, evaluation methodology, and statistical experimentation during the interview process.
• Research Lattice's products and mission to understand how your skills and experience align with the company's goals and values.
• Be ready to provide specific examples of how you have applied AI engineering principles to real-world problems and challenges.
• Consider highlighting any experience you have with MLOps tooling, published research, or open-source contributions in AI/ML to stand out as a candidate.