Madhive is the leading independent and fully customizable operating system built to help local media professionals build profitable, differentiated, and efficient businesses. Madhive empowers sales teams to extend their reach into streaming and connects local advertisers with the communities they serve. Madhive’s platform provides the unique ability to reach local audiences at national scale, with premium supply partnerships and end-to-end tools for planning, targeting, and measuring full-funnel campaign outcomes. Powering campaigns for over 30,000 small and medium businesses per day, Madhive is driving the evolution of local media.
We are seeking an experienced Staff Engineer to join our Maverick AI team. The successful candidate will build dynamic, very low-latency data pipelines and innovate novel, concise, and high-performing architectural improvements. You will lead as we develop best practices and create stability and operational efficiency for our increasingly complex data and database systems.
In this role, you will be a pivotal figure in shaping our technical strategy and execution for Maverick AI. Our data drives the capability of our AI product. Collaboration with our product team and other technology leaders will be central to your responsibilities.

• Drive design and architecture discussions for data infrastructure supporting multiple autonomous AI agents, building alignment on complex decisions around feature serving, dynamic data access patterns, and real-time ML feature serving.
• Design, code, test, and release high-quality software for ML feature pipelines and real-time serving infrastructure, ensuring our AI agents can access features with sub-millisecond latency across 100+ models.
• Run and measure live experiments to optimize data delivery for unpredictable agent behavior patterns, testing hypotheses about caching strategies, pre-fetching, and adaptive pipeline architectures.
• Collaborate with cross-functional teams including AI researchers and ML engineers to understand how different agents generate research requests and design data solutions that scale with their evolving needs.
• Apply your engineering expertise to solve challenges unique to serving large volumes of data to agentic systems, such as handling sudden request spikes when multiple agents converge on similar research areas or managing feature freshness for real-time model inference.
• Support on-call assignments for critical AI serving infrastructure, ensuring feature pipelines maintain reliability even under unpredictable agent-generated loads.
• Mentor junior engineers on the unique challenges of AI infrastructure, from feature engineering best practices to debugging data issues in multi-model serving environments, supporting hiring and evaluation of candidates.
• Write technical documents to influence architectural decisions around our feature store design, agent data access patterns, and strategies for scaling to thousands of models.