Join the LLR family of private equity-backed growth companies.

The Role

This role is designed for former engineering leaders (IC or EM) or founders who are comfortable owning end-to-end technical outcomes but specifically want to continue being impactful as individual contributors and spend more time in the code and solving with business users. This is a full-stack data and analytics role: you'll pull data from our Snowflake data lake or directly from source systems, build out the data structures, marts, and views to support it, and build front-end visualizations that put insight in the hands of the business.

You'll work on a small, high-caliber team (2–3 engineers and with various Product Specialists) building AI, data, and analytics products end-to-end - from data ingestion and modeling through to the visualizations business users rely on. You'll set technical direction, write code, and be the person the team looks to when something is hard.

You'll spend roughly 75% of your time in development and 25% working directly with stakeholders - often their technical leaders - understanding problems, walking through tradeoffs, and making sure what we're building meets their needs.

Most engineers take a decade to see this range of hard problems across this many domains. Here, you'll do it in your first year. To make this possible, we are religious about being the best place to learn Applied AI engineering practices.

What You Bring

• 8+ years of engineering experience, with deep care for the craft. You've shipped complete products end-to-end and write elegant, production-ready code across multiple disciplines.
• Specific interest in applying software engineering fundamentals to AI systems. You care about balancing frontier model capabilities with good system design.
• Full-stack data and analytics chops. You're comfortable pulling data from a warehouse like Snowflake or directly from source systems, modeling it into clean structures, marts, and views, and building front-end visualizations that business users can act on.
• Comfort talking with senior technical stakeholders. You navigate conversations skillfully, and care about people using what you build.
• High ownership mindset. You jump in without instruction, embrace a "no job too big, no job too small" mindset, and want to shape strategy and culture.
• Low ego, high integrity. You help others and ask for help. You hold a high bar for honesty - with yourself and your team.

Our Engineering Philosophy

We build AI systems our own way: bringing the rigor of proven software engineering to the unpredictable nature of frontier AI. We frame our work around hypotheses we can test, build data sets that last, and make sure what we deliver keeps performing long after handoff.

With more than 30 AI products shipped, we've formed clear views on what separates the ones that succeed and what it takes to get them live.

How This Role Is Different

Engineers here typically ship two to three products a year and pick up lessons from dozens more. You'll own each one with real independence, but you won't get a year to perfect any single system. In exchange, you stay constantly close to the latest models and tooling and develop an instinct for AI product development you'd struggle to find elsewhere.

Our Values

• Overdeliver: We're defining how enterprises unlock value from LLMs, and earning a name as a world-class applied AI team along the way.
• Overuse AI: We explore and experiment relentlessly to advance applied AI, and we pass what we learn on to each other.
• Over-"engineer" the Culture: We're shaping a culture that draws and keeps the best applied AI talent, and we pressure-test big decisions against the judgment of our world-class builders.