Applied AI Engineer

About Brandlight

Brandlight is the leading enterprise AI visibility platform, built to help the world’s leading brands understand, manage, and optimize how they appear across AI-driven search, commerce, and advertising.

As AI rapidly becomes a trillion-dollar marketing and distribution channel, Brandlight is defining the systems that decide how brands are surfaced, trusted, and chosen.

Join our team and help build the next generation of tools shaping the future of brand management in an AI-first world.

About the Role

We’re looking for a Senior Applied AI Engineer who combines deep data science expertise with the engineering skills to turn research into reliable, production-ready products.

You’ll be a hands-on technical leader, owning significant AI capabilities from problem definition, academic survey, and system design through research, experimentation, deployment, and continuous improvement. Your work will span classical machine learning, large-scale data analysis, and AI agents that power brand intelligence, market research, and performance marketing.

You should have a track record of driving complex projects, not just contributing to them, and be comfortable making technical decisions, navigating ambiguity, and delivering in a fast-moving startup environment. You’ll build systems that Fortune 500 marketing teams rely on to make consequential business decisions.

Responsibilities

• Own AI capabilities end to end. Translate business and product needs into well-defined problems, research plans, and technical designs. Take solutions from initial exploration through production deployment and ongoing improvement.

• Develop and improve our core algorithms.

• Build production-grade AI agents - performance marketing, market research agents, auto-ML agents.

• Turn research into maintainable software. Build reusable modules, data pipelines, and services with clear interfaces, automated tests, and robust deployment practices—not just standalone prototypes.

• Own quality and performance in production. Monitor system behavior, investigate failure cases, and continuously improve accuracy, reliability, latency, and cost as usage and data volumes grow.

• Drive technical decisions and execution. Choose the right approach for each problem, balancing statistical methods, classical ML, and LLM-based systems. Make explicit trade-offs between research depth, delivery speed, and operational complexity.