Staff Software Engineer, AI

## About the Role As a Staff Software Engineer, AI at Lattice, you will play a pivotal role in shaping the company's AI engineering capabilities. This role involves designing, scaling, and operating AI systems that are at the heart of Lattice's platform. Your expertise will be crucial in defining the technical direction for AI quality, reliability, and evaluation across the organization. Day-to-day, you will work closely with cross-functional teams, including product engineering, to ensure seamless integration of AI capabilities into Lattice's offerings. Given the company's commitment to innovation and excellence, you will be part of a culture that values technical leadership, innovation, and collaboration.

The AI Engineering team at Lattice is responsible for developing and maintaining the intelligence that powers the platform. This includes architecting and scaling the infrastructure that supports AI quality, reliability, and reuse. Your role will be instrumental in setting the standards for AI evaluation methodology, ensuring that AI systems are not only highly performant but also trustworthy and transparent. Lattice's emphasis on AI is part of its broader mission to help people and organizations thrive, making this role both challenging and rewarding for those passionate about AI's potential to drive meaningful change.

Reporting to the engineering leadership, you will be part of a team that values open communication, technical excellence, and a customer-centric approach. Your ability to lead by example, mentor junior engineers, and collaborate with peers across different functions will be essential in driving success in this role. Lattice is committed to fostering an environment where engineers can grow professionally, contribute to impactful projects, and enjoy a healthy work-life balance, especially in a remote work setting.

## What You Will Do - Design and scale an end-to-end AI evaluation framework that includes offline evaluations, production tracing, and human feedback loops to ensure AI quality and reliability.

• Define and implement meaningful performance metrics for AI systems, including task completion rates, hallucination, response quality, engagement, and business impact, and develop automated scoring systems to prevent regressions.
• Identify and quantify drivers of agent quality improvement and establish methodological standards for AI evaluation across the organization to ensure consistency and excellence.
• Architect reusable agent infrastructure using frameworks like LangGraph, focusing on multi-turn workflows, LLM DAGs, recommendation systems, and standardized topologies to enhance scalability and efficiency.
• Build and scale RAG pipelines, vector retrieval systems, and production-grade AI infrastructure with strong reliability, observability, and performance to meet the growing demands of Lattice's platform.
• Make informed build-vs-buy decisions for LLM providers, agent frameworks, and evaluation tooling, considering factors such as capability, cost, latency, and risk to optimize AI system design and deployment.
• Engineer AI systems as reusable internal platforms that multiply product engineering velocity at Lattice, focusing on agility, flexibility, and innovation.
• Own projects end-to-end, from scoping and design to execution and delivery, ensuring that AI projects are well-managed and successfully implemented.
• Set the technical direction for agent quality and evaluation strategy across Lattice's engineering teams, providing guidance and oversight to ensure alignment with company goals.
• Lead rigorous discussions on AI system design and evaluation methodology, fostering a culture of technical excellence and continuous learning within the organization.
• Mentor junior engineers and provide code reviews to raise the AI engineering bar, promoting knowledge sharing and skill development across the team.

• Deep production experience with LLM systems, including prompting, RAG, agent orchestration, evaluation frameworks, and fine-tuning, with a focus on scalability and reliability.
• Experience in building and operating agentic systems, including multi-step workflows and multi-agent topologies, and managing their failure modes to ensure robustness.
• Strong command of AI evaluation methodology and statistical experimentation, with the ability to design and implement experiments to measure AI system performance.
• Production-grade Python skills, with the ability to write clean, maintainable, and testable systems, and experience with relevant tooling and frameworks.
• Experience with LangGraph or comparable agent orchestration frameworks, and LLM observability/evaluation tooling to ensure effective AI system design and operation.
• Strong system design judgment across scalability, latency, accuracy, reliability, and cost, with the ability to make informed technical decisions.
• Experience operating AI systems in cloud environments, including CI/CD, monitoring, and deployment workflows, with a focus on AWS or comparable platforms.
• Familiarity with vector databases and retrieval system design, such as Pinecone or similar technologies, to support AI system development.

• Background in traditional ML and judgment in selecting ML vs. LLM approaches, depending on the problem domain and requirements.
• Experience with MLOps tooling, such as MLflow or DataDog, to streamline AI system development and deployment.
• Published research, talks, or open-source contributions in AI/ML, demonstrating expertise and commitment to the field.
• Experience in HR tech or other trust-sensitive domains, with an understanding of the unique challenges and requirements of these areas.

• Opportunity to work on cutting-edge AI projects with real-world impact, and contribute to the development of innovative AI solutions.
• Collaborative and dynamic work environment, with a culture that values openness, innovation, and teamwork.
• Flexible remote work arrangements, with the ability to work from anywhere and maintain a healthy work-life balance.
• Professional development opportunities, including training, mentorship, and education support to help engineers grow in their careers.
• Access to the latest tools and technologies, and the freedom to explore new ideas and approaches in AI system design and development.
• Recognition and rewards for outstanding performance, and a culture that values and celebrates individual and team achievements.

- Develop a strong portfolio: Showcase your experience in AI system design, evaluation, and scalability, highlighting projects that demonstrate your technical leadership and innovation.

• Stay updated on AI trends: Continuously update your knowledge of the latest developments in AI, including new technologies, frameworks, and methodologies, to remain competitive in the job market.
• Focus on practical skills: In addition to theoretical knowledge, emphasize practical skills in AI engineering, such as programming languages, frameworks, and tooling, to demonstrate your ability to design and deploy AI systems effectively.
• Prepare for technical interviews: Be ready to discuss your experience, skills, and projects in detail, and prepare to solve technical problems or complete coding challenges during the interview process.
• Highlight soft skills: In addition to technical abilities, highlight your soft skills, such as communication, teamwork, and leadership, to demonstrate your ability to work effectively in a collaborative environment.
• Negotiate your salary: Based on your experience and the market rate, be prepared to negotiate your salary to ensure you are fairly compensated for your skills and expertise.
• Research the company: Understand Lattice's mission, values, and culture to ensure alignment with your own goals and expectations, and to demonstrate your enthusiasm for the company and the role during the application process.