About us:

Blue Rose Research builds data and AI tools that help Democrats win elections and advance progressive causes. We forecast races, test ads at scale, and use AI to track what's breaking in the news, then help campaigns respond fast with messages that actually move voters. Our work has guided how hundreds of millions of dollars are spent by the country's top campaigns and progressive organizations. We're a small, mission-driven team of engineers, data scientists, and political strategists who build fast, experiment boldly, and care deeply about using technology for good.

Software Engineering Lead (LLM Products)

The 2028 cycle starts the day after the midterms, and a wide-open Democratic presidential primary will begin almost immediately. With a crowded field and a fragmented media environment, campaigns will need to understand what's reaching and persuading voters faster than ever, and our tools will be at the center of that work from the first days of the primary through the general election.

We're hiring a Software Engineering Lead to set the technical direction for a new generation of AI-powered products: conversational interfaces, autonomous research agents, and tools that turn large amounts of data into information a person can act on in seconds. You'll lead a team of engineers building these systems, making the architecture calls, raising the bar on quality, and growing the people around you, while staying hands-on in the code. You'll partner closely with our political research team, data scientists, and product stakeholders to decide what to build and how to build it well. You'll be joining at the start of the cycle, with time to build systems and a team that are ready before the pressure peaks rather than scrambling to catch up.

You will:

• Lead a team of engineers building end-to-end products where agents and humans work together, from early prototypes through production.
• Own the technical direction and architecture for our LLM-powered systems: agents, retrieval, evaluation, and the infrastructure that keeps them reliable and fast.
• Stay hands-on: write production code, review designs and pull requests, and unblock the team on the hardest problems.
• Set the engineering bar for the team, including code quality, testing, observability, and how we evaluate AI output so people can actually trust it.
• Establish how the team uses AI tools and coding agents to move fast without compromising quality, and know when polish matters versus when it doesn't.
• Work closely with political researchers, data scientists, and other engineers to turn ambiguous problems into clear plans and simple, usable software.